“history matching and uncertainty assessment of the norne field e

Department of Petroleum Engineering and Applied Geophysics

“History Matching and Uncertainty Assessment of the Norne Field E-Segment

Using Petrel RE”

Master’s Thesis in Reservoir Engineering Trondheim, June 2010

Jideofor Odinukwe Celia Correia

NTNU Norges teknisk-naturvitenskapelige Fakultet for ingeniørvitenskap og teknologi universitet Faculty of Engineering and Technology Studieprogram i Geofag og petroleumsteknologi Study Programme in Earth Sciences and Petroleum Engineering

Institutt for petroleumsteknologi og anvendt geofysikk Department of Petroleum Engineering and Applied Geophysics

MASTER OF SCIENCE THESIS

The candidate’s name:

Jideofor Odinukwe

Celia de Amor Gomes Correia

Title of Thesis, English:

“History Matching and Uncertainty Assessment of the Norne E-Segment using PETREL”

Extended text: This study has been done by two candidates; both have worked together as a team and

contributed equally in the completion of this work.

Plan and Scope of work: 1. Building The Norne E-Segment PETREL Model 2. History Matching Of The Norne E-Segment 3. Forward Modelling Of The Reservoir. 4. Volume Calculations In The Norne E-Segment. 5. Uncertainty And Sensitivity Analysis. 6. Impact Of Grid Resolution On Well Placement

Area of specialization: Reservoir Engineering

Combination of subjects: Petroleum Engineering

Time interval: 11th

January 2010 - 24th

June 2010

________________________ Jon Kleppe

Faglærer/Teacher

Original: Student

Kopi: Fakultet

Kopi: Institutt

i

Acknowlegdement

We would like to express our sincere gratitude to our thesis supervisor Professor Jon Kleppe at NTNU for his invaluable advice, guidance and support during the writing of this thesis.

We would also like to thank Truls Skarre and Tor Christian Sandø our industry based supervisors at Schlumberger Information Solutions (SIS) for their guidance and input while working on our thesis, and also invaluable tips on the use of PETREL and its functionalities.

Further we would also like to thank Schlumberger Information Systems (SIS) for their support and trainings they organized for us and STATOIL (operator) of the Norne field and its license partners ENI and Petoro for the release of the Norne data. We also acknowledge the managers of the Norne E-segment data base at IO centre NTNU; Mohsen Dadashpor, Richard Rwechungura and Eka Suwartadi. We‟d also like to thank Fru Blaise of Hess Energy for his suggestions and sharing his reservoir engineering knowledge.

I (Jideofor) would like to thank Mr & Mrs Emeka Onuorah, and Mr & Mrs Chekwube Osonwa for their support both morally and financially during my studies.

I (Celia) would like to express my gratitude to NOMA and my employers Instituto Nacional de Petroleo in Mozambique for financing my studies here in Norway.

We thank our parents (Mr & Mrs Fidelis Odinukwe, and Briolange Magaia), husband and children (Victor Correia, Enzo, Kevin and Celena), siblings (Nicette Gomes Lindstrom, Ada, Chiadi, Anulika, Nkiru, Uche, Chinenye, Chukwudum and Nzube) and loved ones (Sophia Eziuloh) for their prayers and support.

Above all we thank God for his mercies during our studies here at NTNU and for giving us the strength and fortitude to see our program to a successful completion.

ii

Dedication

We dedicate this work to Almighty God who guided and saw us through this work. We also dedicate this work to our families for their support, understanding and prayers during our program.

iii

Abstract

History matching is one of the most important activities during the development and management of

petroleum reservoirs. Matched models are fundamental to ensure reliable future forecasts, and give an

idea of the level of understanding of the geological and reservoir models. Petroleum reservoirs are very

complex and there are great difficulties involved in building correct reservoir models. Depending on

this level of knowledge, available production data, and complexity of a reservoir, this activity may be

very time consuming. In order to achieve matched models, sometimes little changes can be made on the

geological and reservoir models, mainly in those attributes with higher uncertainty, for example, relative

permeability curves, its distribution through the reservoir, and other parameters with few samples.

One of the objectives of this work is to perform history matching on the Norne Field E-segment using

Schlumberger‟s seismic to simulation software Petrel. The history match will be done based on trial and

error by modifying some reservoir properties, end point scaling and by modeling and including the

aquifer support into the system. The best history match was achieved by modifying the SWCR in the

system, modeling an aquifer support using Carter Tracy model and by modifying the horizontal and

vertical Permeabilities of the model. After the history match is achieved two prediction strategies are

developed and compared, where we did forward modeling to build some confidence level in the

reservoir model.

An Uncertainty and sensitivity assessment was done on the reservoir model to analyze the impact certain

reservoir properties have on the volume calculations and simulation results in the Norne E-segment

reservoir model. The impact of uncertainty in our Sw and contact positions on volume calculations in this

model can be seen.

The impact of the grid resolution in placing horizontal wells was also studied, a synthetic model of the

Norne E-segment was built selecting the varying grid resolutions both laterally and vertically, then

properties were upscaled to the models and a well placed below an impermeable shale zone in each

model to determine the optimum location. Using a model with a fine grid resolution enables you to place

the well in the best position in the reservoir model.

iv

Table of Contents

Acknowlegdement ....................................................................................................................................................... i

Dedication................................................................................................................................................................... ii

Abstract ..................................................................................................................................................................... iii

Table of Contents ...................................................................................................................................................... iv

Chapter 1 .................................................................................................................................................................... 1

1.1 Introduction ...................................................................................................................................................... 1

1.2 Thesis Description ............................................................................................................................................. 1

Chapter 2 .................................................................................................................................................................... 3

2.1 The Norne Field ................................................................................................................................................ 3

2.2. General Field Information ............................................................................................................................... 4

2.3. Field Geology ................................................................................................................................................... 5

2.4. Main Processing System .................................................................................................................................. 7

2.5. Water Injection ................................................................................................................................................ 7

2.6. Subsea System and producing wells ................................................................................................................ 7

2.7. Resources And Recoverable Reserves Of Norne Field ..................................................................................10

2.8. Drainage strategy and well plans ..................................................................................................................10

Chapter 3 ..................................................................................................................................................................13

3.1 Case Study - The Norne E-Segment ................................................................................................................13

3.2 Brief Description of the Norne E-Segment .....................................................................................................14

3.3 Provided data .................................................................................................................................................16

3.4 The Reservoir model of the Norne E-Segment ...............................................................................................16

3.4.1 The Eclipse Model ....................................................................................................................................16

3.4.2 The Petrel Model .....................................................................................................................................17

Chapter 4 ..................................................................................................................................................................21

4.1 Manual History Matching ...............................................................................................................................21

4.1.1 Introduction .............................................................................................................................................21

4.1.2 Objectives of Matching Historical Reservoir Performance .....................................................................22

4.1.3 Strategy and Plans for History matching .................................................................................................22

4.1.4 Adjustment of History Matching Parameters ..........................................................................................26

v

4.1.5. Special considerations in History Matching ...........................................................................................30

4.2 Automatic History Matching (AHM) ...............................................................................................................31

When To Use Automatic History Match ...........................................................................................................32

4.3. History Matching the Norne field E-Segment................................................................................................33

4.3.1 General ....................................................................................................................................................33

4.3.2 Properties Modified During History Matching Process ...........................................................................35

4.3.3 Results .....................................................................................................................................................58

Chapter 5 ..................................................................................................................................................................60

5.1 Prediction .......................................................................................................................................................60

5.2 Multi-segmented well model .........................................................................................................................60

5.2.1 Benefits of using multi segmented wells .................................................................................................61

5.3 Forward Modeling ..........................................................................................................................................64

5.4 Multi-segmented well option .........................................................................................................................69

Chapter 6 ..................................................................................................................................................................74

6.1 Uncertainty Assessment .................................................................................................................................74

6.1.1 Introduction .............................................................................................................................................74

6.2 Volume Calculations .......................................................................................................................................75

6.2.1 Volume Calculation Method ....................................................................................................................75

6.2.2 Workflow for Volume Calculation ...........................................................................................................77

6.3 Workflow for Uncertainty Assessment and Sensitivity analysis .....................................................................79

6.3.1 Uncertainty Assessment ..........................................................................................................................79

6.3.2 Sensitivity Analysis ...................................................................................................................................82

Chapter 7 ..................................................................................................................................................................90

7.1 Impact Of Grid Resolution On well Placement ...............................................................................................90

7.1.1 Optimum Well Placement .......................................................................................................................90

7.1.2 Impact of Grid Resolution during Well Placement. .................................................................................93

Chapter 8 ..................................................................................................................................................................99

Conclusions ...........................................................................................................................................................99

Recommendations ..............................................................................................................................................100

Bibliography ............................................................................................................................................................101

Nomenclature .........................................................................................................................................................103

List of figures ..........................................................................................................................................................105

vi

List Of Tables ..........................................................................................................................................................107

Appendix A .............................................................................................................................................................108

Tables with modified parameters performed in Petrel 2009.2 ..........................................................................108

Tables with Simulation Runs ..............................................................................................................................109

Property Calculators ...........................................................................................................................................111

The Producers on the E-Segment .......................................................................................................................112

The Injectors on the E-Segment .........................................................................................................................113

Appendix B ..............................................................................................................................................................114

Well Trajectory ...................................................................................................................................................114

Production and Injection data ............................................................................................................................124

Well Events .........................................................................................................................................................128

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“History Matching and Uncertainty Assessment of the Norne Field E-Segment” M.Sc. Thesis June 2010

Chapter 1

1.1 Introduction History matching is one of the most important activities during the development and management of petroleum

reservoirs. Matched models are necessary: to ensure reliable production forecasts and to increase the confidence

in understanding the geological and reservoir models.

Petroleum reservoirs are very complex structures and several uncertainties are present in the process promoting

low confidence in reservoir simulation models. During the first years of production, uncertainties still play a

major role in the process making it more complex and allowing multiple acceptable solutions. As production

increases, the quantity of data permits better matching but the complexity of the process increases because of the

high number of wells and objective functions of the optimization process, which usually are the difference

between simulation results and observed data for pressure and each of the production phases. These different

levels of uncertainty and procedures, as functions of reservoir development stages, add an extra complexity to the

usual procedures (6). Uncertainty analysis is very important when making quick and value added decisions while

developing new fields and managing older ones, in situations where capital expenses and subsurface risks are high

uncertainty analysis can be a daunting task.

1.2 Thesis Description The objectives of this thesis include:

To build a Reservoir Simulation Model of Norne field on PETREL using field and well data from Norne Eclipse input data files.

History Match the PETREL model by modifying properties, including relative permeability curves, end points, aquifer properties etc.

Place new production well and build a prediction scenario to build some confidence interval in the reservoir model.

Use multi-segmented Well option. Perform Uncertainty analysis on the Reservoir model. Run Sensitivity analysis on the uncertainties. Build a synthetic model going through an upscaling process, selecting the correct grid

resolution both laterally and vertically. Upscale Properties to one or more models. Place well optimally using appropriate grid resolution.

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This work contains 8 chapters in total; chapter 2 introduces and describes the Norne field. The major

sections in this chapter gives the general field information, talks about the field geology, main

processing system, the recoverable reserves of Norne field and the drainage strategy.

Chapter 3 describes the Norne E-segment, the data used in the project, the eclipse reservoir model and

finally the Petrel model and process in which it was built.

Chapter 4 discusses history matching types, procedures and considerations made while history matching.

The history matching process done on the E-segment using Petrel is also discussed here, parameters

modified and effects.

In chapter 5 two prediction scenario‟s on our history matched model is presented and compared, also use

of the multi-segmented well option is presented and discussed.

Chapter 6 deals with Volume calculation in the Norne E-segment, uncertainty and sensitivity analysis of

the model and impact certain parameters have on volume calculations.

In Chapter 7 we have looked at the impact of grid resolution during well placement, three synthetic

models with different vertical grid resolutions have been built and a well has been placed at different

positions to find an optimum location for the well, also in this chapter different methods of optimizing

well placement has been discussed.

Finally the conclusions and recommendations of the study are summarized in chapter 8.

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Chapter 2

2.1 The Norne Field The block 6608/10 in the Norne field is the largest discovery on the Norwegian continental shelf in more

than a decade.

The field is located 85 kilometers from Heidrum and roughly 200 km from the north of the Norwegian

coast. This area has a water depth of 380 meters. Hydrocarbons in the Norne field are located in the

lower-to Middle-Jurassic sandstones, which are of a good reservoir quality. Norne lies in a license which

was awarded in 1986, and embraces blocks 6608/10 and 6608/11[2]. Hydrocarbons in the Norne field are

located in the Lower- to Middle-Jurassic

sandstones, which is of a good reservoir

quality.

The Norne field is owned by a partnership

of : Petoro AS (54.0%), Statoil (39.1%)

and Eni Norge AS (6.9%). Statoil is the

operator[13].

The well 6608/1 0-2 first penetrated the

Norne reservoir in December 1991.

Appraisal well 6608/1 0-3 was drilled in

1993 and proved the field‟s northerly

extension. Based on results from those two

wells, a development project began in

1993. Exploration well 6608/1 0-4 was drilled in a separate smaller structure north-east of Nome and

proved some additional reserves.

An oil zone of 110 meters thick with an overlying gas cap makes up the hydrocarbon column. The

reservoir is a flat structure with the crest about 2,525m below mean sea level (MSL). Reservoir pressure

is close to hydrostatic, with a formation pressure of 273bar and a temperature of 98°C at a reference

depth of 2,639m below MSL. The oil/water contact is defined at 2,688m. Reserves in-place is estimated

at one billion barrels (160 million m³) of oil and 29 billion m³ of free and associated gas.

Figure 2.1 The Norne Field [3]

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Reservoir simulations and risk analysis suggest that the most likely estimate for recoverable reserves is

450 million barrels of oil and 15 billion m³ of gas.

Production from the Norne field in the Norwegian Sea began on 6 November 1997. Recoverable

reserves originally present were 89.2 million Sm3 oil, 14.billion Sm3 gas and 1.7 million tonnes NGL.

Out of which according to reports, remaining at 31.12.05 were 21.4 million Sm3 oil, 9.4 billion Sm3 gas

and 1.2 mill tonnes of NGL. Estimated production in 2006 was 76,000 bbl/day oil, 1.2 billion Sm3 gas

and 0.16 million tones of NGL.[2]

2.2. General Field Information

The field has two separate compartments:

Norne Main Structure (Norne C, Norne D and E-segment)

Relatively Flat with generally a gas filled Garn Formation and the gas oil contact in the vicinity of

the Not formation clay stone. The Norne main structure includes 97% of the oil in place.

Northeast Segment (Norne G-Segment)

The northen flank dips towards north-northwest with an oil leg in the Garn Formation.

Figure 2.2 Map of Norne field. The 4 main fault blocks are denoted C. D, E and G[3]

5



2.3. Field Geology

The reservoir is around 3 x 9 km in extent, at a depth of approximatelly 2.5 km bellow the sea surface at

its shallowest; it comprised of lower and midle jurassic sandstones in a NE/SW trending horst block.

The gas cap is approximatelly 75 m thick with an oilcolumn of 110 meters. Sub-horizontal shale and

calcite permeability barriers and faulting have a major impact on gas and water injection and on

reservoir production. The reservoir blocks are separated by 4 main fault blocks. The Oil and gas is

contained in Jurassic sandstones with good reservoir qualities; porosity ranges from 20-30% and

permeability ranges from from 50md-1000md. Oil is mainly found in the Ile and Tofte formations, and

the gas in the Garn formation. The cap rock which seals the reservoir and keeps the oil and gas in place

is the Melke formation. The Not formation also behaves as a cap rock, preventing communication

between the Garn and the Ile Formations.

The present geological model consists of 17 reservoir zones. Today‟s reservoir zonation is slightly

altered from earlier subdivisions. An ilustration of the zonation from 2001 can be seen in figure 2.

Figure 1. shows the geological zonation from 2002 and 2006.

Norne 2002 Norne 2006

Lower Melke Not 3 Upper Not Shale Garn 3 Not 2 Not 2.3 Not Sist Garn 2 Not 2.2 Garn 1 Not 2.1

Not Not 1 Lower Not Shale Ile 2 Ile 2.2 Ile 2 Ile 2.2 Ile 2.2.2

Ile 2.2.1 Ile 2.1 Ile 2.1

Ile 1 Ile 1.3 Ile 1 Ile 1.3 Ile 1.2 Ile 1.2 Ile 1.1 Ile 1.1

Tofte 2 Tofte 2.2 Tofte 2 Tofte 2.2 Tofte 2.1 Tofte 2.1

Tofte 1 Tofte 1.2 Tofte 1 Tofte 1.2 Tofte 1.1 Tofte 1.1

Figure 2.3. Old and new zonation of the Norne Field[4]

6



Figure 2.4 Stratigraphical sub-division of the Norne reservoir (StatoilHydro, 2001) [4]

7



2.4. Main Processing System

The well stream will be transferred via the swivel mounted in the turret to the inlet separator, operating

at 15-20bar. Oil from this separator is stabilized in a second separation unit, operated at 1.5-2bar, before

it is transferred via a coalescer to a storage tank. Gas from the second-stage separator is compressed in

two stages, then mixed with gas from the inlet separator. All the gas is then compressed in three stages

to 280bar, for its reinjection into the reservoir.

2.5. Water Injection

De-aeration of the injection water has been eliminated, since the presence of oxygen in the injected

seawater is expected to stimulate reservoir productivity. Produced water will also be re-injected into the

reservoir. Together, with the reduced use of chemicals owing to the elimination of de-aeration, this

solution will help to safeguard the environment. Injecting raw seawater, together with the produced

water, has simplified the water-injection system, but has also required the extensive use of high-quality

materials.

2.6. Subsea System and producing wells

Subsea production facilities will comprise five well templates - three for production, one for water

injection and one for combined gas and water injection. Each template has four slots and the capacity to

tie in additional satellite wells. Flexible flow lines and risers are specified. A multifunctional umbilical

will be used to control and monitor the subsea system, to distribute chemicals and hydraulic fluid, as

well as to supply power. The templates are being installed in northern and southern groups, placed about

4,000m apart. Water depth varies between 370-390m. One production and one water-injection template

will make up the northern group. These installations are tied back to the production ship by two nine-

inch production lines, one nine-inch water-injection line and one control and service umbilical. The

southern group comprises two production templates, a combined water-/gas-injection line and two

control and service umbilical‟s. The templates in each group are positioned so that the rig can enter all

the slots without the need for anchor handling.

8



Figure 2.5. The Norne Sub-Sea system[2]

The field is being developed with five templates (B, C, D, E and F) at the sea bottom connected to a

floating production vessel. In 2005 an extra template K was placed on the sea bottom 150-200 meters

south of B, C and D templates. The K-template has 4 slots available; 3 for producer and 1 for injector or

producer. The first production well K-3 H is planned to be drilled during summer of 2006.

In January 2006 the Norne Field is producing oil from all 12 well slots, aproximately 15000 Sm3/d. 8

injectors have been drilled, and water is injected in all 8 wells. 68 mill Sm3 oil has been produced since

the production staterd, which is approximately 43% of the oil in place or 76% of recoverable reserves.

Figura 2.6 Development of the Norne Field [Statoil, 2001] [4]

9



Table 1. Active development wells in the Norne Field ( NPD fact pages 2009)

Well Name Completion date Drill Permit Wellbore Purpose Wellbore Contents

6608/10-B-1 BH 2006 2634-P Production Oil

6608/10-B-2 H 1997 1239-P Production Oil

6608/10-B-3 H 1999 1590-P Production Oil

6608/10-B-4 DH 2004 2423-P Production Oil

6608/10-C-1 H 1998 1422-P Injection Water



6608/10-C-4 AH 2004 2342-P Injection Water

6608/10-D-1 CH 2003 2335-P Production Oil

6608/10-D-2 H 1998 1249-P Production Oil

6608/10-D-3 BY2H 2005 2580-P2 Production Oil

6608/10-D-3 BY1H 2005 2580-P1 Production Oil

6608/10-D-4 AH 2003 2218-P Production Oil

6608/10-E-1 H 1999 1591-P Production Oil

6608/10-E-2 CH 2008 2915-P Production Oil

6608/10-E-3 CH 2005 2551-P Production Oil

6608/10-E-4 AH 2000 1727-P Production Oil

6608/10-F-1 H 1999 1584-P Injection Water



6608/10-F-4 AH 2007 2898-P Injection Water

6608/10-K-1 H 2006 2772-P Production Oil

6608/10-K-3 H 2006 2743-P Production Oil

6608/10-K-4 H 2007 2830P Production Oil

6608/10 3103-P Production Oil

6608/10 3106-P Production Oil

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2.7. Resources And Recoverable Reserves Of Norne Field The most likely in place volumes and official recoverable reserves for the Norne Field are:

Table 2.2 Initial volumes in place Oil and Gas Description Units PDO Official RNB 2006

Oil in Place, STOIIP X 106 Sm3 164.2 157.0

Gas in Place (free & Solution) X 109 Sm3 29.9 29.8

Table 2.3 The NPDs reserve estimates as of 31.12.2009

Recoverable Reserves Remaining Reserves

Oil 94.70 mill Sm3 Oil 12.00 Mill Sm3

Gas 10.50 bill Sm3 Gas 4.50 Mill Sm3

NGL 1.60 mill Tonn NGL 0.90 Mill Sm3

Cond 0.0 Mill Sm3 Cond 0.0 Mill Sm3

Approximately 80% of the of the initial oil reserves in place on the Norne Main structure are located in

the Ile and Tofte formations. The free gas is primarly located in the Garn Formation.

2.8. Drainage strategy and well plans

The Norne Field should be regarded as a mature reservoir and plateau rates will definitely not be

achieved anymore. The reservoir performance is in major parts of the field as expected. Water cut is

rising in most of the wells. Drainage from the upper Ile reservoir has started in 3 wells, two of them

performing well. The reserve estimate in base case has been increased from 88.5 to 89.2 million Sm3.

This is based on ivestment of the K-template and the verification of TTRD Technology in 2005.

11



However, the increase in reserves is less than the increase due to these IOR measures due to a reduction

of the estimate for reserves.

The Field is now developed only with horizontal producers. However, to accelerate the build-up of well

potential until plateau production was reached, some of the first producers were drilled vertical to some

deviated. All these wells have been sidetracked to horizontal producers.

The pre-start drainage was to maintain the reservoir pressure by re-injection of produced gas into the gas

cap and water injection into the water zone. However, during the first year of production it was

experienced that the Not shale is sealing over the Norne Main Structure, and the gas injection has been

changed to inject in the water zone and the lower part of the oil zone, and in 2005 the gas injection was

ended. The horizontal oil producers in Tofte and Lower Ile formation will be plugged and sidetracked

and drilled horizontal in upper Ile Formation just below the Not Formation shale when the water cut

becomes high (>90%) resulting in problems to lift the liquid.

Figure 2.7 Cross-Section Area of the Norne field [5]

12



The Norne field is a flat horst structure and a change in fluid contacts during production has to be

monitored by the difference is seismic signals from the reservoir zone from year to year. In this context

it is very important for repeatability that the seismic lines are acquired at the same geographical position

each time and the seismic acquisition parameters are identical to the previous surveys. For the 4D

surveys on the Norne Field, WesternGeco Q-marine active streamer steering have been used, allowing

accurate positioning of streamers for reliable repeat surveys2.

Figure 2.8 Norne Field drainage pattern

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Chapter 3

3.1 Case Study - The Norne E-Segment The Norne E-segment is part of the Full Norne Field which was separated from it for different study

purposes by the Integrated Operations Center IOC at NTNU to be used in the Petroleum Industry.

Figure 3.1 The E-segment showing the oil saturation and the wells

Producers

Injectors

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3.2 Brief Description of the Norne E-Segment

The Reservoir model consists of 8646 (8733) active grids and it has 7 wells (2 injectors and 3 producers

with 2 side tracks). E-Segment is composed of 11 X 52 X 22 cells in the I, J and K direction

respectively. It is also composed of 10 faults and 23 horizons.

Figure 3.2 Faults of the E-Segment

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Figure 3.3 One of the horizons of the E-Segment

Table 3.1 Faults on the E-Segment

Fault Name Horizontal Length (m)

M West 3512.98

M_North 2517.62

DE_2 29233.77

E_01 2602.93

DE_1 2123.41

EF 1850.72

E_01_F3 546.304

DE_B3 608.384

DE_0 689.798

16



3.3 Provided data Initially the first package data was released in May 2009 to be used by selected researches within the IO

Center; this includes users from NTNU, Standford University, TNO, TU Delft and Sintef. Basically

there are two kinds of data supplied; production data and seismic data. Also reports showing different

development stages of the field have been provided. The released data includes:

1) Reservoir simulation model of E-segment in Eclipse format, updated 2003.

2) All wells including well logs.

3) The reservoir is been producing since 1997, oil rates, water and gas rates are available for each

well and BHP are also recorded.

4) Production data for each well up to 2006 are given for history matching purpose.

5) 4D seismic data in 2001 is used as base case and two monitors in 2003 and 2004 for E-segment

are available.

6) Difference in saturation and pressure in each cell from 2003 and 2004 inverted seismic data.

3.4 The Reservoir model of the Norne E-Segment

3.4.1 The Eclipse Model

The Norne E-segment Reservoir model is a black oil model with a gas cap and a strong aquifer, the

blocks consist of 11 by 55 grids with dimensions of approximately 134m in the X direction and 91m in

the Y direction. The model has 22 layers with an average height of 8.69m each and about 12826 active

cells. The geometry of the field has been modeled using corner-point geometry. The field has been

developed using three oil producers (E-2H, E-3H, E-3AH) and two water injectors (F-1H, F-3H).

The oil water contact (OWC) is at 2613.62m, gas oil contact (GOC) 2581.16m and a reference depth of

2355m with a pressure of 277 bars at that depth. The main properties of the Reservoir used in the

simulation are as summarized below.

17



Table 2-2 Oil and Gas Properties

Oil and Gas Properties Value Unit

Oil Formation Volume Factor at Pb 1.344` Rm3/Sm3

Stock Tank Oil Density 860 Kg/m3

Oil Viscosity at Pb 0.576 cP

Surface Gas Density 0.853 Kg/m3

Gas Viscosity 0.0224 cP

Table 2-3 Water Reservoir Properties

Properties Value Unit

Water Compressibility 4.67 E-5 1/bar

Water Formation Volume Factor 1.038 Rm3/Sm3

Water Viscosity 0.318 cP

Water Density 1033 Kg/m3

3.4.2 The Petrel Model

Petrel is Windows based software for 3D visualization, 3D mapping and 3D reservoir modeling and

Simulation. Different reservoir simulators like Eclipse and Frontsim amongst others can be run on Petrel

and visualized, but models not created on Petrel limits its functionalities a lot. Building a Petrel case for

the Norne E-segment was therefore an important first step. The Petrel model is in many ways similar to

the Eclipse model with one major difference which is in the way the development strategy section of

both models are built. The history data, well trajectory and events data from the eclipse section had to be

extracted from the schedule section of the eclipse file and imported into Petrel to build the wells and

prepare the development strategy. Petrel does not recognize the well placement method used in Eclipse

where the trajectory of the well is traced by the centre point of the grid it is passing through. The

trajectories used in drilling the wells were gotten from Statoil and used in building them into Petrel.

18



Figures 3.4 to 3.6 below shows the producers from Norne E-segment on Petrel and Eclipse respectively

notice the difference in their paths.

Figure 3.4 Well E-2H in Petrel and Eclipse respectively

19



Figure 3.5 Well E-3AH in Petrel and Eclipse respectively

Figure 3.6 Well E-3H in Petrel and Eclipse respectively

20



Figure 3.7 Norne Field as in Eclipse Model

Figure 3.8 Norne Field E-Segment as in Petrel Model

Petrel Model

Eclipse Model

21



Chapter 4

4.1 Manual History Matching

4.1.1 Introduction

The history matching process constitutes a crucial phase in a reservoir study. The objective is to build a

model integrating all available data to reduce the uncertainties on reliable production forecasts. The

model must therefore not only reproduce production data by numerical simulation but it must also be as

consistent as possible with the geological knowledge of the reservoir.

To obtain a match of dynamic data, the most commonly used method consists in modifying the

parameters of the initial model, proceeding by trial and error. The initial model is usually based on

geological knowledge and field measurements. The consistency of the model must therefore be checked

against the initial geological description during the matching procedure.

The goal of a numerical –model study is the prediction of reservoir performance in more detail and with

more accuracy than is possible with simple techniques such as extrapolation. It is intuitively evident that

for a model to behave like the reservoir it must be conceptually similar to the reservoir. Significant

differences between the data defining the reservoir in the numerical model and the actual values of the

parametres governing reservoir performance will cause correspondingly significant errors in the

simulation. Unfortunatelly we seldom know enough about a reservoir to develop an acceptably accurate

model without testing it in some way and alterring it‟s properties until it passes the test. The most usefull

and usually the only available-way to test the model is to simulate past performance of the reservoir and

to compare the simulation with actual, historical performance. Modeling past performance will identify

weakness in data, suggest modifications that are needed to improve the model, and demonstrate the

quality of the reservoir description that is eventually accepted. If the changes that are made in the model

to force it to simulate historical performance are consistent with a comprehensive and rational

description of the reservoir, this process of matching history can be an especially useful and powerful

reservoir description technique.

22



History matching can be time consuming, expensive, and frustating because reservoir performance can

be complex with enumerous interactions that as a whole, may be difficult to comprehend. To make the

process manageable, it is useful to separate it into a number of specific, individual steps. While there is

no approach to history matching that is universally applicable, there are a few techniques that lend some

structure to the matching process.

4.1.2 Objectives of Matching Historical Reservoir Performance

The primary objective of history matching is to test and to improve the reservoir model. There are

secondary objectives, some of which may become apparent only as history matching proceeds. History

matching will contribute to an understanding of the current status of the reservoir, including fluid

distribution and fluid movement and , peharps, to verification and identification of the current depletion

mechanism. It will allow one to infer a reservoir description, including oil and gas in place, in parts of

the reservoir where there are no data, and will supply details needed to justify plans and to estabilish

objectives for obtaining data. Sometimes history matching can lead to discovery of a major operating

problem – casing leaks, improper allocation of fluids to wells, etc. In addition a well matched model that

is kept current can be an excellent reservoir surveillance tool.

4.1.3 Strategy and Plans for History matching

A core analysis and log data can define porosity and initial fluid distribution if the data are available in

sufficient quantity and quality. Permeability data usually are too limited in quantity, both areally and

vertically, to describe the reservoir adequately. Analysis of transient behavior of individual wells is a

standard technique for determining permeability around the well. For developing improved definition of

the permeability distribution of the reservoir as a whole, history matching is the method most commonly

used. Aquifer data are always extremely sparse, and is frequently necessary to use history matching to

define aquifer porosity, transmissibility and extent.

23



4.1.3.1 Performance Data To Be Matched

In general, the data that are matched are pressure, water/oil ratio‟s (WOR‟s), gas/oil ratios (GOR‟s),

water/gas ratios (WGR‟s), water and gas arrivel times, and fluid saturations from cores, well logs, and

chemical tracer tests.

Periodic observations of shut-in bottomhole pressures are almost always available. Bottomhole flowing

pressures (BHFP‟s) are also available at times, but these data are usually less reliable than shut-in

pressures for history-matching purposes. Shut-in surface pressure can be useful if accurate fluid levels

and gradients are available to correct pressures to bottomhole conditions. Pressure interference data can

be especially useful if they are accurate enough to yeld independent estimates of between-well

permeability-thickness.

Matching historical WOR‟s, GOR‟s or WGR‟s is usually the best way to confirm the validity (even

though history matching may not identify zones that are not being swept effectivelly) of estimates of

effective zonation and zonal continuity. When the reservoir is on an early stage of depletion, or if for

any other reasons there are no direct data defining water or gas movement, one must rely heavely on

core analysis, logs, and knowledge of the depositional envinronment to develop zonation and to estimate

continuity.

Field-measured production and injection rates are normally used without alteration. There are some

special situations, however, where it may be appropriate to assume that historical producton or injection

rates are in error and to adjust them.

Oil production rates are usually the most accurate data available. Gas production in older fields may not

have been measured accurately, especially if the gas has been flared. Injection data tend to be less

accurate than production data, either because measurement errors or because fluids are lost to other

intervals as a result of casing leaks or flow behind pipe. Errors in production data may occur for the

same reasons, but they are usually discovered and corrected. If volumes are measured at central sites and

not at individual wells, allocation back to wells will be a potential source of error. Where production or

injection is commingled, allocation to individual zones will be a source of error.

24



4.1.3.2. Action Steps in a History Match

The steps one would normally follow in conducting a match include the following:

Assemble data on performance history;

Screen the data and evaluate their quality;

Define the specific objectives of history match;

Develop a preliminary model based on the best available match;

Decide weather the model is satisfactory. If not-as is most probable-analyze results with

simplified models or “hand” calculations to identify changes in model properties that are most

likely to improve the agreement between observed and calculated performance;

Decide weather an automatic matching program should be used:

Make adjustments to the model. Consult with geologic, drilling, and production operations

personnel, as necessary, to confirm the realism of proposed changes;

Simulate part or all of past performance data to improve the match;

Reapeat simulations until a satisfactory match of observed data is achieved.

4.1.3.3 General Strategy for History matching

Although each reservoir study will supply its own unique set of problems during a history match, the

following general strategy is usually effective:

Match Volumetric-average pressure levels. This is a first step toward confirming the overall

compressibility of the reservoir system.

It is frequently wise to obtain a history match for a complex reservoir in two stages, which could be

categorized roughly as a “gross phase” and a “detailed phase”. In the gross phase, all the history-

matching dependent variables, including pressures, saturations, water or gas arrival times, and any other

field data, should be brought within reasonable but not tight tolerances. In this phase, a more coarsely

gridded model may sometimes be used to reduce overall study costs.

25



During this first stage, the average reservoir pressure throughout time, regional pressure gradients, and

well pressures are matched. To match average reservoir pressure, the most commonly adjusted reservoir

parameters are aquifer size, PV, and total system compressibility (and its constituents). During this

stage, material-balance studies and aquifer influx studies can aid in obtaining insight into the data

adjustments required for the history match. When matching reservoir pressures, any adjustments made to

PV affect the determination of in-place fluid volumes.

Matching individual well pressures throughout time can be attempted also in this stage. In full-field-

simulation, shut-in or buildup pressures should be used and the earliest detected problems should be

corrected first, which will prevent early-time errors from growing and masking later problems. If

pressures are matched adequately early in the life of the well but the match progressively deteriorates,

adjustments to reservoir data away from the wellbore may be required.

After this first phase has been completed, matches of pressure, contact movement, and fluid arrival times

should be brought to closer tolerances.

In the second stage, which is the most frustrating part of any simulation study because changes made to

match individual well rates may affect the quality of the match obtained during the first stage or the

quality of the match in other areas of the field. The well histories that are matched during the second

stage are water cuts, GOR‟s and breakthrough times. Because of the natures of these data all knowledge

of the reservoir geology and recovery processes must be incorporated into the study during this stage.

The objectives, of course, are to avoid making changes that are inconsistent with geological and

engineering data and to facilitate discovery of errors in field data[14].

4.1.3.4. Judging the Acceptability of a model

A general comment has been made, and not entirely in jest, that “a good history match is obtained when

you run out of either time or money”[1]. A much better guideline is to judge the quality of the match by

weather the reservoir model is good enough to permit the objectives of the study to be met. Quality tests

of a match in pressure history are generally applied at the field level, at some areal subdivision of the

field, and at individual wells. In general, the best match is expected at the field level; average field

pressures may differ from average model pressures by only a few pounds per square inch. The quality of

the match will generally be poorest at the subregion or individual well level.

26



Pressure matches for individual wells are often acceptable if model and well pressures agree to within

±50 psi [±345 kPa] on the average. In some reservoirs, of course, such as most gas reservoirs and many

high-permeability reservoirs, a match must be much better than ±50 psi [±345 kPa] to be acceptable.

One way matching criteria can be developed is to estimate the accuracy with which a specific parameter

– pressure-must be predicted if the study is to satisfy its objectives. The accuracy of the history match

should be better than, or at least comparable to, the accuracy required in predictions. This same approach

can be used to evaluate weather an improved match would affect predictions enough to make the extra

work worthwhile.

4.1.4 Adjustment of History Matching Parameters

Making changes by guessing or by following one‟s intuition can be expensive and usually will prolong

the history-matching phase of a study. The decision to use such an unstructured approach may result

from the impression that experienced reservoir engineers develop a “feel” for the “art” of history

matching.

There are numerous papers on automatic history matching. Nevertherless, the majority of engineers

engaged in reservoir simulation use manual rather than automatic methods of history matching because

of the limitations and expense of currently available automatic methods. In fact, in most large reservoir

studies some manual adjustment is almost always required, even if automatic methods are used.

4.1.4.1. Parameters That Can Be Changed To Match History

Those reservoir and aquifer properties appropriate for alteration, in approximate order of deacreasing

uncertainty are: (1) Aquifer transmissibility, kh, (2) Aquifer storage, (3) reservoir transmissibility,

Relative permeability and capilary pressures functions.

The following additional properties must sometimes be altered, but they usually are known with

acceptable accuracy: (5) reservoir porosity and thickness, (6) structural definition, (7) rock

compressibility, (8) reservoir oil and gas properties, (9) water/oil contacts (WOC‟s) and gas/oil contacts

(GOC‟s), and (10)water properties.

27



4.1.4.2. Matching Pressure History

The first step in the pressure history match is to list all those properties of the reservoir and aquifer that

are most likely to affect the pressure history match and estimate a bounds of uncertainty for the

properties listed above. Further Steps are: Develop a criteria by which to judge the acceptability of a

pressure history match;

Complete a trial simulation of reservoir history and decide whether the volumetric average pressure of

the entire reservoir is satisfactorily matched by the model, in case is not, use material-balance, pressure

transient theory, and geologic information to estimate changes that should be made to model value of

fluids in place in the oil zone and gas cap, average aquifer properties, and aquifer size. In this stage also

reconsider concepts of the current depletion mechanism and, in particular, ascertain whether water influx

is playing a major role in performance history.

If the match is not satisfactory, analyze pressure distribution in and near the reservoir to find evidence

for heterogenous aquifer properties and nonuniform water influx. Also look for differences in pressure

distribution between the model and the field that imply the presence of sealing faults, pichouts, poor

comunication between zones and migration to or from other reservoirs.

Also change reservoir and aquifer properties (storage and transmissibility) areally with the use of the

pressure-trasient theory. Pressure errors that appear shortly after production or injection rates change can

be reduced by altering properties in or near the reservoir. Errors that are delayd in time can be reduced

by changing properties in regions more remote from the reservoir.

Errors in pressure gradients can also be corrected by locating where pseudosteady-state conditions exist,

by adjusting transmissibility.

During pressure match there are situations in which matches in pressure gradients are satisfactory for

early history but become poorer as production continues. In simulations of reservoirs producing by water

drive or water injection, this behavior may indicate errors in fluid mobilities. In this case me be

necessary to modify rock or pseudo-relative permeabilities.

28



In general during pressure matching, permeability is the principal reservoir variable used to obtain a

match of pressure behavior. Porosity values derived from log and core data should not be changed unless

the data are sparse and of poor quality or changes in other, more uncertain, properties do not provide a

satisfactory match. Fluid contacts and fluid properties are usually established before history matching

begins and, in many cases, may be better defined than porosity. Porosity thickness in the aquifer is

usually less well known than in the reservoir itself and, within limits, aquifer porosity-thicness or the

areal extent of the aquifer may be varied to achieve a match. In general, however, permeability is the

least well defined and most effective reservoir parameter to vary. Some permeability values may be

considered relatively certain, especially those measured by a well test. Even these values may be

innacurate, however, if an overly simplified reservoir model is used in interpreting the well test data.

4.1.4.3. Matching Gas and Water Movement

Matching fluid movement usually is the strongest verification of the validity of assumptions concerning

reservoir description and reservoir mechanics. It is seldom possible to match water and gas moviment

adequaly without having a model of the reservoir that is reasonably complete and is almost entirely

consistent with both the assumed reservoir description and observed production history.

The best approach to use in matching fluid-contact movement, gas and water arrivel times, and

subsequent GOR and WOR behavior will be very specific to the reservoir being studied.

The first step in the gas and water movement match is to list all those properties of the reservoir and

aquifer that are most likely to influence movement of water and gas and estimate a bounds of uncertainty

for the properties listed above, then develop a criteria by which to judge the quality of a match of gas

and water movement and decide whether matching the behavior of groups of wells rather than individual

well is sufficient.

Another important step is to analyze the reservoir-depletion process to determine wheter coning or

cusping has influenced arrival times of gas and water and subsequent GOR or WOR behavior.

Also there‟s a need to decide whether the relative permeability data should be changed, in case this data

was obtained in measurements at reservoir conditions on representative core samples whose wettability

had been preserved, such change should be avoided. If relative-permeability data are not considered to

be realiable, both the shapes of the curves and end-point saturation can be changed. However, extreme

29



changes that move relative-permeability data outside the range observed in other reservoir studies should

be avoided.

Vertical-permeability values can be important in calculating displacement efficiency and vertical sweep

efficiency and, thus, can affect calculated gas and water arrivel times. Unfortunately, effective vertical

permeability is usually not available from field measurament and cannot be estimated reliably from core

measurements.

It is also important to look at the performances of selected wells to see wheter gridblock definition is a

major problem. Coarse gridding can create apparent differences in model and field behavior because

gridblock centers may not be too far from actual well locations, or because poor definitions causes errors

in calculated displacement efficiency or sweep efficiency.

While making changes in the model properties to match fluid movements it‟s advise to continue to

compare calculated and actual pressure behavior.

Matching performance of a well in which water underlies (or gas overlies) the completion interval

almost always requires use of a coning model. The model is adjusted to match history by varying the

permeability levels in a key layer(s) such as a tar-bearing zone, where permeability is not well known, or

in a low-permeability zone, which severely limits vertical flow. Vertical permeability between the

completion interval and WOC or GOC is almost always a critical matching parameter. But it is also

important to model horizontal permeability correctly so that pressure drawdown is matched accurately.

In the absence of coning, cusping, and severe stratification, models may predict water production that is

not observed in the field. Obviously, water relative permeability is not properly defined, possible

because: saturation change in the field is following a hysteresis loop, and drainage rather than imbibition

water relative permeabilities should be used at low water saturations; water relative permeabilities at low

saturations are incorrect (laboratory measuraments at low saturations are difficult and relative

permeabilities at saturations up to about 20% PV above irreducible water saturation probably will not

have been measured), or the initial water-saturation distribution in the model is incorrect. Laboratory

experiments may be necessary to evaluate whether one of the explanations should be accepted for a

specific reservoir system.

30



4.1.5. Special considerations in History Matching

4.1.5.1. Evaluating the Quality of field data.

Good rate and pressure data from the field are essencial to obtaining a good reservoir model. These data

should be plotted on a well-by-well basis to identify and to remove any obvious data inconsistencies.

When beginning a history-match phase, it is useful to get as close as possible to the source of data. If

timing will permit, personally collect the data in the field office. Screen the data but is advisable not to

discard data

In general, it‟s always important to be aware of the quality and relative accuracy of the rate and presuure

data. Gas and oil production rates from gas/oil separation plants or by lease are usually quite accurate.

However, allocation of these rates to the individual wells is generally much less accurate. Water

injection rates are often less accurate than oil production rates. Casing leaks or leaks through bad cement

jobs can sometimes reduce or increase the rates of water flowing into a formation; the existence of such

things is often detected in history matching. It‟s ussually necessary to confirm that measured pressures

have been corrected to datum properly.

Equally important are data on WOC‟s, GOC‟s, and original oil in place (OOIP) or original gas in place.

Initial values of these quantities should be compared with exixting estimates. If there are differences,

they should be resolved before a simulation goes beyond the initialization stage. Generally, in a field

with a long history, there will be values, especially for OOIP, that have been estabilished by prior

studies and that should not be changed without adequate reason. For young reservoirs, there may not be

a good estimate of OOIP. In this case, care must be taken tomake sure that simulator values are

consistent with whatever data are available.

4.1.5.2. Correcting Observed Pressures to model conditions

Pressure data for active production and injection wells normally will not correspond directly to

calculated pressure in the gridblocks containing the wells. For instance, calculated pressure in a

gridblock containing a producing well may be above the BHFP but less than the pressure measured after

a 72-hour shut-in period. The reason for the apparent discrepancy, of course, is that the horizontal

31



dimensions of a gridblock are much larger than the radius of the well and, hence, the gridblock pressure

corresponds to an actual pressure some distance from the wellbore. For adequate comparison it is

necessary to adjust either the calculated or measured pressure so that the two pressures represent

conditions at the same location in the reservoir. In practice, it usually is more convenient to adjust the

measured pressures. [1]

4.2 Automatic History Matching (AHM)

Automatic history matching is identical to manual history matching except that computer logic is used to

adjust the reservoir data rather than direct engineering intervention. It generally uses inverse simulation

that involves output least squares algorithms. These algorithms are based on minimizing an objective

functional, a quadratic function of the differences between observe and predicted measurements.

Gradient-based algorithms are then used to speed up the process of parameter estimation. Constraints

and priori information are added to restrict the dimension of the parameter spaces. Finally sophisticated

search algorithms involving trust region methods are employed for the constrained optimization

problem. Thus the automatic history matching becomes a mathematical minimization problem.

Reservoir history matching problems are generally characterized by a very large number of unknown

parameters. Consequently, the efficiency of numerical minimization algorithms is a primary concern. In

addition, these problems are typically ill-conditioned; many quite different sets of parameter estimates

may yield nearly identical matches to the data (Ewing., 1994). Because of these concerns, much research

is yet to be done, and at the current stage of development automatic history matching is of limited use

for practical problems.[14]

Most automatic history-matching methods do not permit simultaneous matching of two or more kinds of

data. In practice, pressure normally would be matched first. Then GOR or WOR would be matched by

automatic modification of either rock or pseudo-relative-permeability functions. Most experience to date

has been on matching pressure data, but the selection of the type of data to vary is an option. For a match

of pressure, the model grid generally will be divided into arbitrary areas and the simulator will modify

multiplying factors-one value for each area- that will raise or lower all permeabilities and/or porosities

uniformly for each area. Hence, the reservoir variables or parameters that are automatically changed to

32



achieve a match are the multiplying factors rather than the permeabilities or porosities themselves. It is

generally not feasible to have a multiplying factor for every permeability or porosity value in the model

(Chen et al). [15]

The procedures for history matching will depend somewhat on the characteristics of the computer

program, but generally will include the following steps: Firstly is to devise a numerical model of the

reservoir, and if water drive is significant, the model should simulate the aquifer in addition to the

reservoir. Then select the variables to be modified by the automatic history matching package. It is

important to be judicious in the selection of variables and to select only those variables that have the

strongest effect on the objective function. There‟s a need to set the constraints so that the AHM program

will not be allowed to produce wild or unrealistic values of reservoir parameters. Then assign the best

current reservoir description to the model by setting up the observed data and assigning scaling factors

to the observed data. At any stage of the AHM process judicious changes in variables and variable areas

or regions have to be done on the basis of experience and understanding of reservoir mechanics.

When To Use Automatic History Match

One detrimental feature of the AHM process is that it excludes the engineer from the history-matching

phase of the simulation study. Consequently, the use of AHM may remove engineering judgment and

specific knowledge of the subject reservoir from the history match.

Currently, AHM is not widely used and probably should not be the method of choice for most problems.

AHM should be considered, however, if manual matching is not proceeding satisfactorily. If automatic

matching is considered then one should follow some guidelines for it. The AHM should be used only

after making a number of manual history-matching trials to identify important matching variables, then

use the matching program to identify the remaining weak variables that have little effect on the quality

of the match. Make carefully estimates of computing costs before embarking on an automatic matching

phase. Above all is not recommended to proceed blindly when using automatic history matching

simulator, follow closely the progress of the regression as a match is approached and be well aware of

the value of the sum of squared differences of each new trial.

33



Selection of which history-matched method, manual or automatic to use in the history matching phase of

a simulation study depends on the objectives of the history match, the company resources devoted to the

history match and the deadlines of the simulation study.[17]

4.3. History Matching the Norne field E-Segment

4.3.1 General

The most common procedure to perform a production history matching is to start with a base model and modify

reservoir and fluid properties to adjust simulation results with the production history of the field.

The data to perform the history matching of the Norne E-Segment was accessible via the web-page and

includes a reservoir simulation model in eclipse format, a geological report describing the stratigraphy of

the field consisting of 17 zones, and petrophysics reports from three wells which provides data related to

permeability, water saturation, net-to-gross, thickness, porosity, capillary pressure, porosity-permeability

relationship in all formations. There is also history data available such as production data and well logs.

As described in chapter 3 the data used for the history matching was imported into Petrel (the procedures

are described there) from the eclipse model available. The data available for the history matching is

composed of Oil, water and gas production history data for the 3 wells and water injected for the 2

Injectors, starting from 1998 to 2004. No field pressure history data was available.

34



Table 4.1 Wells data available

Well Name Type of Well Start Production End Production

E-3H Producer 01 August 1998 01 May 2000 (stop production)

E-2H Producer 01 Nov 1999 01 Jan 2004 (still producing)

E-3AH Producer 01 Dec 2000 01 Jan 2004 (still producing)

F-1H Injector 01 May 1999 01 Jan 2004

F-3H Injector 01 Sept 2000 01 Jan 2004

Figure 4.1 FOPR for the base case and the history

35



Figure 4.2 FWPR for the base case and the history

4.3.2 Properties Modified During History Matching Process

One of the first steps during history matching is to match the pressure. However since there was no

pressure history data available no match was done on field pressure.

In the absence of any history pressure data we have attempted to match field oil, water and gas rates,

cumulative oil produced and also Field GOR‟s.

4.3.2.1 End Point Scaling

The first modification done was by doing some end point scaling. The ECLIPSE saturation table End-

point Scaling option allows us to redefine values for the connate, critical and maximum saturations in

the saturation tables describing the flow of the reservoir fluids. The scaling facility is useful for

modeling reservoirs which contain an initial depth variation of either the connate or critical saturations

for one or more of the phases present. It has applications in the use of pseudo functions and in general

cases where the saturation function data for the reservoir fluids depend on a normalized saturation

variable.

36



The End-point Scaling option is enabled by the ENDSCALE keyword in the RUNSPEC section.

Separate re-scaling is done on relative permeability and capillary pressure curves. Generally, each is a

two-point rescaling, two saturation values in the tables being moved to new positions. There is also an

option to apply a three-point rescaling to the relative permeability curves only. In a three phase model

there are eight saturation table end points which may be identified [10]:

Table 4.2 Saturation table end points Keyword Description

Keyword Description

SWL The connate water saturation. This is the smallest water saturation entry in a water saturation table.

SWCR The critical water saturation. This is the highest water saturation for which the water is immobile.

SWU The maximum water saturation. This is the largest water saturation entry in a water saturation table.

SGL The connate gas saturation. This is the smallest gas saturation entry in a gas saturation table.

SGCR The critical gas saturation. This is the highest gas saturation for which the gas is immobile.

SGU The maximum gas saturation. This is the largest gas saturation entry in a gas saturation table.

SOWCR The critical oil-in-water saturation. This is the highest oil saturation for which the oil is immobile in an oil-water system

SOGCR The critical oil-in-gas saturation. This is the highest oil saturation for which the oil is immobile in an oil-gas-connate water system

We shall refer to the saturation end points taken from the saturation tables in the input data file as the

unscaled saturation end points.

The End-point Scaling option enables you to define new values for any of the above eight saturation end

points for each grid cell, subject to maintaining a consistent set of saturation tables within each grid cell.

The appropriate subset of the above eight end points is applicable in two phase runs. The new values

defined using the scaling option will be referred to as the scaled end points.

When relative permeability and capillary pressure values need to be computed at a particular saturation,

a linear transformation is used to determine the equivalent saturation to be used for look-up in the

unscaled table [10].

37



The Norne Field has 22 saturation tables, the end point scaled here was the SWCR (Critical water

saturation, which is the highest value of Sw on the saturation table for which the value of Krw is zero)

which the initial value was in a range from 0.0975 to 0.2780 and been scaled to a range of 0.28 to 0.38

(see figure 1A of app.).

Figure 4.3 Relative Permeability curves (before (1) and after simulation (22 and 91)) for different

values of SWCR

38



Figure 4.4 History Matching of FOPR after changing SWCR

Figure 4.5 History Matching of FGOR after changing SWCR

39



Figure 4.6 History Matching of FOPT after changing SWCR

Figure 4.7 History Matching of FGPR after changing SWCR

40



Figure 4.8 History Matching of FWCT after changing SWCR

4.3.2.2 Adding an Aquifer

The next we added an aquifer to model the pressure support and run it. Aquifer modeling is a method of

simulating large amounts of water (or gas) connected to the reservoir whereby it is not essential to know

how the fluid moves in it, but rather how it affects our reservoir.

There are several aquifer models: numerical, Carter Tracy, Fetkovich, constant flux, constant pressure

(gas or water) and rainfall. Each aquifer model has its own set of parameters and can be connected to the

grid in different directions: top down, bottom up, grid edges and/or fault edges. To have better control

over which cell needs to be connected, a series of options can be used to limit the vertical extent and to

restrict the connections to filtered cells only [9].

In Petrel to add an aquifer model, first we need to select the area where we want to add it then select the

drive mechanism. The area selected covers the whole reservoir and the drive mechanism is from bottom

up and grid edges. The aquifer model chosen was Carter Tracy which is a simplified approximation to a

fully transient model, which avoids the need for superposition. The model uses tables of dimensionless

time versus a dimensionless pressure influence function [9].

41



Two main constants govern the behavior of the aquifer; 1. The time constant which is a function of the

aquifer permeability, the aquifer porosity, the viscosity of the water in the aquifer, the total

compressibility (rock + water) and the outer radius of the reservoir. 2. The dimensionless time constant.

The time constant is used in converting time to the dimensionless time constant.

The initial aquifer pressure used is that of the reservoir so that the aquifer and reservoir will be in

hydrostatic equilibrium with each other. The angle of influence used and aquifer internal radius are 360o

and 500m respectively.

Figure 4.9 The Norne E-Segment before adding the Aquifer

42



Figure 4.10 The Norne E-Segment after adding the Aquifer

Figure 4.11 History Matching of FOPR after adding an aquifer

43



Figure 4.12 History Matching of FGOR after adding an aquifer

Figure 4.13 History Matching of FOPT after adding an aquifer

44



Figure 4.14 History Matching of FGPR after adding an aquifer

Figure 4.15 History Matching of FWCT after adding an aquifer

45



4.3.2.3 PERMZ and PERMX

The third parameters changed were the PERMZ and PERMX around the wellbore. The initial value for

PERMZ was in the range of 0 to 1885 then it was reduced to a range from 0 to 179, using the property

calculator, meaning that it was multiplied by 0.05. As for PERMX the initial value was in a range of 8 to

2175 and it was modified to a range from 7 to 2066, which was multiplied by 0.95.

Figure 4.16 History Matching of FOPR after changing PERMZ and PERMX

46



Figure 4.17 History Matching of FGOR after changing PERMZ and PERMX

Figure 4.18 History Matching of FOPT after changing PERMZ and PERMX

47



Figure 4.19 History Matching of FGPR after changing PERMZ and PERMX

Figure 4.20 History Matching of FWCT after changing PERMZ and PERMX

48



4.3.2.3 Relative Permeabilities Kro, Krw

Permeability is the ability or a measurement of a rock‟s ability and capacity to transmit fluids, often

measured in Darcy (D) or milli-darcy (md). It is usual to differ between absolute, effective and relative

permeability.

Absolute Permeability: Is the measurement of the permeability conducted when a single fluid or phase

is present in the rock.

Effective permeability: Is the ability to preferentially flow or transmit a particular fluid through a rock

when the other immiscible fluids are present in the reservoir.

Relative Permeability: Relative Permeability is the ratio of effective permeability of a particular fluid

at a particular saturation, to absolute permeability of that fluid at a total saturation.

kro=ko/k

Where: kro: is the oil relative permeability

Ko: is the permeability of oil

K: is the absolute permeability

Oil and water relative permeabilities in an oil-water system are usually plotted as a function of water

saturation. The Sor is called the residual oil saturation. The directions of the curves point out the

saturation histories which are called drainage and imbibitions. The drainage curve applies to processes

where the wetting phase is decreasing in magnitude. The imbibitions curve applies to processes where

the wetting phase is increasing in magnitude.

Relative Permeability Curves:

The relative permeability curves consist of three elements: The end point fluid saturations; the end point

permeabilities and the curvature of the relative permeability functions.

Figure 4.21 Typical curves for oil-water relative permeability at water wetted system

49



The end point saturations determine the movable saturation range and are directly related to amount of

recoverable oil. The end points of relative permeabilities enter in the expression for the mobility ratio

and will determine sweep efficiency of a displacement process. The shape of the curves in between may

also have an important bearing on recovery efficiency.

M=krw*μo/kro*μw

Where:

M: Mobility ratio

kro: is the oil relative permeability

krw: is the water relative permeability

μo: oil viscosity

μw: water viscosity

Two-Phase relative Permeability models

The relative permeability for a two phase models are generally obtained from laboratory measurements

on suitable cores. In some cases these data may be missing and reasonable approximations necessary.

These approximations are called models when they are available in algebraic form. The most commonly

used correlation is the Corey‟s two-phase model and are obtained by Corey correlation using the

following equations (oil-water system):

Where:

= Normalized/effective-Oil-Saturation

Where:

=Normalize/effective-Water-Saturation

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Figure 4.22 Oil/Water and Oil/Gas relative Permeability Curves

It is possible to scale the relative permeability at the maximum phase saturation and at the

critical/residual saturation of the associated phase. The scaled relative permeabilities can be specified on

a block by block basis, or alternatively the depth variation can be specified using the ENKRVD and

ENDNUM keywords. The KRW, KRG, KRO keywords and their derivatives are used to set the relative

permeabilities at the maximum phase saturation, while the KRWR, KRGR, KRORW, KRORG

keywords and their derivatives are used to set the relative permeabilities at the critical/residual saturation

of the associated phase.

The last parameter changed was the maximum relative permeabilities Krw and Kro on the saturation table.

Petrel only allows changing the maximum values of the Real Perms on the saturation tables. The values

can only be changed in an ascending order for Krw and in descending order for Kro,

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Figure 4.23 Relative Permeability curves before and after changing the maximum values of Kro and Krw

52



Figure 4.24 History Matching of FOPR after changing Krw and Kro

Figure 4.25 History Matching of FGOR after changing Krw and Kro

53



Figure 4.26 History Matching of FOPT after changing Krw and Kro

Figure 4.27 History Matching of FGPR after changing Krw and Kro

54



Figure 4.28 History Matching of FWTC after changing Krw and Kro

4.3.2.5 Running the simulation with all modified parameters

After running the parameters individually, we decided to run all parameters (SWCR, adding the aquifer,

PERMZ and PERMX and Krw, Kro) that were changed together and the result was a better match.

Figure 4.29 History Matching of FOPR after combining the changed parameters

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Figure 4.30 History Matching of FGOR after combining the changed parameters

Figure 4.31 History Matching of FOPT after combining the changed parameters

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Figure 4.32 History Matching of FGPR after combining the changed parameters

Figure 4.33 History Matching of FWCT after combining the changed parameters

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History Matching on the Wells

Figure 4.34 History Matching of Well E-2H after combining the changed parameters

Figure 4.35 History Matching of Well E-3AH after combining the changed parameters

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Figure 4.36 History Matching of Well E-3H after combining the changed parameters

4.3.3 Results

Changing of SWCR

By changing SWCR the end points will therefore change. If we look at the figure 4.3-4.6 above we will

see that there is a slight change in the original case, therefore this parameter affects the field oil and gas

production rate, Field Oil Cumulative Produced but has little influence on GOR.

Adding an aquifer

By adding an aquifer model we are therefore modeling the pressure support of the whole reservoir.

Figure 4.11-4.15 shows that there is a major change in the field oil and gas production rate, field oil

cumulative produced and GOR. Therefore this modification gives a better much compared to the base

case.

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PERMZ and PERMX

After adjusting PERMZ and PERMX, using different combinations on the multiplier a better match

using these parameters was achieved using the values 0.05 and 0.95 respectively (see figures 4.18-4.22).

During the runs when using values bellow 0.05 and 0.5 respectively we would get erros and

convergence problems. For the case of GOR the better match is achieved from the Period of December

1999 to March 2001. This parameter has a second better match compared to the one when we add the

aquifer.

Change in Kro and Krw

The kro and krw were the last parameters to change. By changing both parameters the end points will be

changed as well (figure 4.23), so has the mobility ratio. Looking at the figure 4.24-4.28 we can observe

that there is a small change on the field oil and gas production rate, on the Oil cumulative produced and

no change on GOR.

Combining SWCR, Aquifer, Kro, Krw, PERMZ and PERMX

By combining all the adjusted parameters a better match was achieved (figure 4.29-4.33) at Field level

for oil, gas and water produced as well as for GOR and Water Cut. A better match was also achieved at a

wells level (see figure 4.34-4.36).

From all the plots above regarding the field oil production the better much is located on two distinct

periods as from August 1999 to November 2000 and also from April 2001 to December 2003. As for

GOR the better match was achieved from March 2002 to December 2003 which is the period where we

start having a decrease in oil production rate.

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Chapter 5

5.1 Prediction A fundamental task of the reservoir engineer is to adopt techniques to predict future production rates for

a given reservoir or a specific well. The objective of history matching is to build a reservoir model that

integrates available data and yelds production forecasts that are accurate. For these forecasts to be as

reliable as possible, a necessary condition is to ensure that the model encompasses all available data and

information.

5.2 Multi-segmented well model

The Multi-segment Well model is a Special Extension, which is available in both ECLIPSE 100 and

ECLIPSE 300. It provides a detailed description of fluid flow in the well bore. The facility is specifically

designed for horizontal and multi-lateral wells and to model „smart‟ wells containing various types of

flow control device,, although it can of course be used to provide a more detailed analysis of fluid flow

in standard vertical and deviated wells. Like the standard well model, the equations are solved fully

implicitly and simultaneously with the reservoir equations, to provide stability and to ensure that

operating targets are met exactly.

Each segment may have completions in one or more reservoir grid blocks, or none at all if there are no

perforations in that location. The variables within each segment are evaluated by solving material

balance equations for each phase or component and a pressure drop equation that takes into account the

local hydrostatic, friction and acceleration pressure gradients. The pressure drop may be calculated from

a homogeneous flow model where all the phases flow with the same velocity, or a „drift flux‟ model that

allows slip between the phases. Alternatively, the pressure drop may optionally be derived from pre-

calculated VFP tables, which can potentially offer greater accuracy and provide the ability to model

chokes.

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5.2.1 Benefits of using multi segmented wells

The Multi-segment Well model has all the capabilities of the Wellbore Friction option, and provides

several additional features and greater flexibility. The benefits of using this well model include:

1. Improved handling of multi-lateral topology: Data input has been designed for ease of use with

multi-lateral wells. There are two separate keywords to define the segment structure and the perforated

lengths. Individual branches are identified by their branch number. Branches may, if required, have sub-

branches.

2. Improved modeling of multi-phase flow: Use of the drift flux model or pre-calculated pressure drop

tables can produce more accurate pressure gradients than the homogeneous flow treatment used in the

other well models. The pressure gradient can vary from segment to segment throughout the well; it is

calculated fully implicitly in each segment using the local flowing conditions.

3. Flexibility: The variable number of grid block completions per segment allows the choice between

using many segments for greater accuracy or fewer segments for faster computation.

4. The Multi-segment Well model contains several facilities for modeling advanced wells. Specific

segments can be configured to model flow control devices such as chokes, by providing a pressure drop

table that describes their pressure loss characteristics as a function of flow. Various „built in‟ flow

control device models are also provided:

a. Variable pressure loss multipliers can be used to represent „smart‟ devices which react to

isolate high GOR or WOR regions.

b. Flow limiting valves can also be used to represent „smart‟ devices which react to limit the

flow of oil, water or gas through a segment.

c. Other advanced well components such as sub-critical valves, „labyrinth‟ devices and down-

hole separators can also be modeled.

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5. Crossflow can be modeled more realistically, as the fluid mixture can vary throughout the well.

Complex crossflow regimes may potentially occur in multi-lateral wells, including branch-to-branch

crossflow and crossflow within individual branches. In the other well models, if crossflow occurred, the

mixture flowing back into the reservoir would reflect the average contents of the well bore, regardless of

which parts of the well were crossflowing.

6. Wellbore storage effects can be modeled more accurately by dividing the well bore into several

segments. The drift flux model allows phases to flow in opposite directions at low flow rates, so that

phase redistribution within the well bore can occur during shut-in well tests.

A Multi-segmented well can be considered as a collection of segments arranged in a gathering tree

topology, similar to the node-branch structure of a network in the Network option. A single-bore well

will, of course, just consist of a series of segments arranged in sequence along the well bore. A multi-

lateral well has a series of segments along its main stem, and each lateral branch consists of a series of

one or more segments that connects at one end to a segment on the main stem (Figure 5.1). It is possible,

if required, for lateral branches to have sub-branches; this may be useful when modeling certain inflow

control devices as part of the network of segments.

The segments network for each well may thus have any number of „generations‟ of branches and sub-

branches, but it must conform to gathering-tree topology. Segments within the gathering tree topology

can be connected in order to form loops.

Figure 5.1 A multi-lateral, multi-segmented well

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63



Each segment consists of a node and a flow path to its parent segment‟s node. (We use the term

„flowpath‟ here rather than „branch‟, as the latter term is reserved for lateral branches in multi-lateral

wells, each of which may contain several segments.) A segment‟s node is positioned at the end that is

furthest away from the wellhead (Figure 5.2). Each node lies at a specified depth, and has a nodal

pressure which is determined by the well model calculation. Each segment also has a specified length,

diameter, roughness, area and volume. The volume is used for wellbore storage calculations, while the

other attributes are properties of its flow path and are used in the friction and acceleration pressure loss

calculations.

Also associated with each segment‟s flow path are the flow rates of oil, water and gas, which are

determined by the well model calculation.

Figure 5.2 Well Segments

A segment node must be positioned at each branch junction. Flow from the formation through grid-

block-to-well connections also enters the well at segment nodes. A segment (or to be more precise, its

node) can accept flow from any number of grid block connections (Figure 5.3). If you wish to have a

separate segment node for each connection (for greatest accuracy) then it is best to position the nodes to

lie at the center of their corresponding grid block connections. But it is also possible to reduce the

number of segments by allocating two or more grid block connections to each segment In this case it

best to position each segment node to lie at the center of the perforated interval that is allocated to the

segment.

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ECLIPSE allocates each grid block connection to the segment whose node lies nearest to it. If necessary,

you can over-ride this by explicitly specifying the segment to which the connection should be allocated

in the COMPSEGS (or COMPSEGL) keyword.

Figure 5.3 Allocating connection flows to segments

It is also possible to position segment nodes at intermediate points along the well bore, for example

where the tubing properties or inclination angle change. Additional segment can be defined to represent

chokes or inflow control devices. In common with other aspects of numerical simulation, the optimum

number of segments required to model a particular well depends on your preferred compromise between

speed and accuracy.

5.3 Forward Modeling

After History Matching the Norne E-Segment, a basis for predicting future production and further

development of the field is now available. Two prediction scenarios are analyzed here:

Producing the field for another five year‟s using the present development strategy and the wells

currently in place.

Placing a new well and producing from there while shutting in the present production wells.

At the end of our simulation run in January 2004 producers E-2H and E-3AH are still open for

production in the Norne E-segment while pressure support is achieved using the injectors F-1H and F-

3H. The field has been simulated another 5 years till 2009 to predict the recovery from the field in 2009,

table 1 below shows the details of the development strategy used.

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Table 5.1 Development Strategy

WELLS RATE (Sm3/d) BHP (Bars) TIME

E-2H 2400 200 2005-2009

E-3AH 600 200 2005-2009

F-1H 7000 - 2005-2009

F-3H 4500 - 2005-2009

In the second case a new oil producer J2 has been placed to produce in place of wells E-2H and E-3AH

but F-1H remains open to provide pressure support to the reservoir. Table 2 below shows the prediction

strategy used, figure 5.1 shows the FOPR for case 1 while figure 5.4 below shows FOPR for case 2.

Figure 5.3-5.5 compares the 2 cases in terms of cumulative oil produced, field oil in place and field gas

in place as of December 2009.

Table 5.2 Prediction Strategy

WELLS RATE (Sm3/d) BHP (Bars) TIME

J2 350 200 2005-2009

F-1H 4500 - 2005-2009

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Figure 5.4 FOPR with Current Development Strategy

Figure 5.5 FGPR with Current Development Strategy

67



Figure 5.6 FOPR with New Development Strategy

Figure 5.7 FOPR with New Development Strategy

68



Figure 5.8 FOPT with New Development Strategy

Figure 5.9 FOIP with New Development Strategy

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Figure 5.10 FGIP with New Development Strategy

5.4 Multi-segmented well option The effect of using the multi-segmented well option was analyzed using well E-2H as a case study. Due

to the absence of a flow performance table the pressure drop in each segment has not been modeled.

Plotting the simulated pressure with and without well segmentation shows a slight difference (figure

5.11) which maybe due to the pressure drop from friction in the wellbore being considered during the

simulation. This difference however disappears when viewing the WOPR as seen in figure 5.12 below.

Figures 5.13 to 5.16 are PLT plot showing simulated flow along the wellbore section where we have our

perforations and the multi-segmentation has been done, the rate, velocity and pressure at each point in

the well can be seen at any given time step. Comparing figures 5.13 and 5.15 shows a large difference

between the oil rate and the water rate at this section of the well, this is because influx into the well from

the perforations are majorly from the oil in this zone and water presence here is minimal. This simulated

PLT data can be plotted against observed PLT data if available and can be used for a good history

matching process.

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Figure 5.11 Field Average Pressure with and without Multi-segmented well option

Figure 5.12 FOPR with and without Multi-segmented well option

71



Figure5.13 Oil Rate at 01/12/1999 Figure 5.14 Oil Velocity at 01/12/1999

72



Figure 5.15 Water Rate at 01/12/1999 Figure 5.16 Pressure at 01/12/1999

73



Figure 5.17 Oil hold up fraction Figure 5.18 Gas holdup fraction

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Chapter 6

6.1 Uncertainty Assessment 6.1.1 Introduction

“The ability to make quick yet intelligent and value-added decisions to develop new fields has always

been of great significance. In situations where the capital expenses and subsurface risk are high,

carefully analyzing the inherent uncertainties in the reservoir and how they impact the predicted

hydrocarbon accumulation and production becomes a daunting task. The problem is compounded in

offshore environments, especially in the presence of heavy oils and disconnected sands, where the

margin for error is small. In many cases, realizing the value of a prospect is very time-dependent and

resolving uncertainties of complex reservoir systems needs a systematic and efficient approach”(16). We

have attempted to present the case study of the uncertainty and sensitivity analysis workflow for the

hydrocarbon accumulation in the Norne E-Segment as a tool for sound development decision and better

History match.

Among the key uncertainties in reservoir engineering parameters is the relative permeability to water,

the position of the Oil water contact (OWC) and the Gas oil contact (GOC), the Net to Gross ratio

(N/G), the porosity amongst others, while the key uncertainties in the volume calculation besides the

parameters mentioned above are the So and Sw. With so many uncertainties working at different scales, it

becomes essential to have a consistent and efficient way of incorporating them into our analysis.

Ranking the uncertainties based on their impact on reserves helps to prioritize/ guide future data

gathering and uncertainty reduction efforts. Assigning probabilistic ranges to key uncertainties also

enables the computation of probabilistic reserves. The use of experimental design and Monte-Carlo

simulation techniques, along with the use of fast parallel reservoir simulation can greatly improve our

uncertainty analysis efforts and help make quality decisions in a timely manner. Calculation of the

STOIIP and the HCPV requires knowledge of the water saturation, oil saturation, net to gross, porosity

and Bo which are all uncertain parameters to various degrees. Uncertainty analysis, even at this early

stage, can determine where resources should be directed to get the most cost effective investigation.

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6.2 Volume Calculations

“Calculating accurately the volumes in a 3D grid (bulk, pore and fluid) is very important because these

figures will often be used as first indications of the economic viability of the field, and together with an

uncertainty analysis, can determine where efforts in reservoir evaluation should be concentrated.

Volume calculations are often left to the last phases of a reservoir investigation when detailed property

modeling has been completed.”(9) Inputs to a volume calculation include:

A 3D grid: A 3D grid with zones and segments so as to calculate the volume in each zone or

segment.

Contacts: An Oil-Water Contact or/ Gas-Oil Contact

Properties: General properties like NTG and Porosity must exist and then properties specific to

the oil or gas zone such as Water Saturation, Oil Saturation, Gas Saturation.

Constants: If there is free gas in the oil or free oil in the gas then constants like GOR, OGR, Bo

and Bg.

6.2.1 Volume Calculation Method

“Each side of the cell is triangulated and the cells are split exactly where they are cut by contacts or

boundary polygons. Because of the geometry of the 3D grid used in Petrel and Eclipse, faults form an

integral part of the grid. Cell boundaries always form the faults in the 3D grid, and volumes are therefore

always reported exactly along segment boundaries.”(9)

Figure 6.1 The basics of calculating volumes in Petrel(9)

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The basic formulas used for volume calculations in this project include:

Bulk Volume = Total Rock Volume

77



6.2.2 Workflow for Volume Calculation

The Volume Calculations have been done using the volume calculation process in PETREL. Here

volumes can be calculated within zones, segments and user defined boundaries using filters and

polygons. In this project the volume calculation has been restricted to zones only as we are looking only

at the E-segment of the Norne field.

Volume Calculations can be done using various hypotheses in one operation; each hypothesis is known

as a run. Running a volume calculation creates a volume case which can be used as the filter for showing

the results of the run in the plot window.

A volume calculation case was created to calculate HCPVoil, HCPVgas, STOIIP, Net Volume, Bulk

Volume and Pore Volume in PETREL. The Hydrocarbon interval to be calculated was specified by

inputting the GOC (2581.16m) and the OWC (2613.62m), the Net to Gross (NTG) and porosities used

are gotten from the 3D grid. The oil and gas properties for the run also need to be specified, which as

stated above include water saturation, gas saturation, oil saturation, oil formation volume factor (Bo), gas

formation volume factor (Bg). The Bo (1.25) and Bg (0.004746) used were taken at the reference depth

(2581m) and pressure (260 bar), with the NTG, porosities, OWC, GOC, Sw/Sg/So, Bo and Bg specified

the volume calculation is ready to be done. The volumes are calculated and the results presented zone by

zone, figures 1-3 show the STOIIP, HCPVoil, HCPVgas through 6 where most of the STOIIP is located.

From our volume calculations and as shown below we have a large gas accumulation in zones 1,2 and 3

indicating the location of our gas interval, zone 4 is a shale zone and has no hydrocarbon accumulation

while zones 5 and 6 consist primarily of oil and has a larger HCPVoil accumulation than the other zones.

78



Figure 6.2 STOIIP (106 Sm3)

Figure 6.3 HCPVOil (106 Rm3)

79



Figure 6.4 HCPVgas (106 Rm3)

6.3 Workflow for Uncertainty Assessment and Sensitivity analysis

6.3.1 Uncertainty Assessment

A base case volumetric and simulation model has been built; this model can be defined as the most

likely representation of the Norne E-segment dynamic reservoir model. We have attempted to:

Measure the effect of variations in the input data on the base simulation case.

Measure the sensitivity of the volume calculations to input parameters

The uncertainty analysis process has been run using the Uncertainty and Optimization workflow in

PETREL, uncertain parameters in the model have been varied randomly at the same time at each run.

The result of this may yield a very pessimistic case for one of the processes, but a more optimistic case

for another process and in some cases incomplete simulation results due to errors which are usually

80



caused by convergence problems while running the simulations producing a more realistic uncertainty

since the probability of getting the extreme low case of each case is very unlikely. Firstly we identified

the parameters we wanted to define as uncertain and then we chose a distribution law and value range to

be used for the uncertain parameters. In our simulation model we have investigated PERMZ and

PERMX as uncertainties and tried to model the impact varying them would have on simulation result.

PERMX and PERMZ multipliers have been created, namely PERMX multiplier and PERMZ multiplier.

The base case for the multipliers has been assumed as one (1) and the variables are 0.8, 0.7, 0.6, 0.5 and

0.4; figures 6.1-6.4 show the impact it has on the FOPR and FGPR.

Figure 6.5 Field Oil Production Rate using all multipliers

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Figure 6.6 Gas Oil Produced using all multipliers

Figure 6.7 Field Gas Production Rate using 0.4 multiplier

82



Figure 6.8 Field Oil Production Rate using 0.4 multiplier

6.3.2 Sensitivity Analysis

As mentioned earlier volume calculations in this project have been zonal with emphasis on zones 1-6.

During the uncertainty and sensitivity analysis on the volume calculations five key uncertain parameters

have been used (GOC, OWC, So, Sw and Porosity), after the ranges of the parameters were defined, 25

volumetric sensitivity runs were made on PETREL; 5 runs for each variable to test the impact each

parameter has on the calculations. Figures 6.9 and 6.10 are histograms showing the variations in STOIIP

for zones 1 and 5 as the variables change with each run. The cumulative distribution curves for the

volumes are also shown on the histograms. The histogram and cumulative distribution curve for each of

the variables can be seen in figures 6.9-6.15; these cumulative distribution curves are also known as

expectation curves where probabilistic approach is used to quantify uncertainty. As the variables change

with each run so does the STOIIP, plotting an expectation curve the uncertainty in the variable can be

expressed based on 0-100% probability scale.

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Figure 6.9 STOIIP Zone 1

Figure 6.10 STOIIP Zone 5

84



Figure 6.11 Porosity

Figure 6.12 So

85



Figure 6.13 Sw

Figure 6.14 GOC

86



Figure 6.15 OWC

All the key uncertain parameters in the volume calculation and their ranges in terms of low and high

STOIIP, HCPVoil, HCPVgas, GIIP values have been tabulated as seen in table 6.1 below. Porosity

multiplier has been used since it has been used in the simulation runs to change the value of our

porosities.

Table 6.1 High and low ranges used on the parameters

LOW High

PORO Multiplier 0.05 0.95

So 0.1 0.9

Sw 0.1 0.9

GOC -2591.16m -2571.16m

OWC -2623.62m -2603.62m

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Based on the simulations from the various scenarios a tornado plot has been made to show the maximum

and minimum value of the volumes calculated for the field. Figures 6.16-6.19 show sensitivity of the

volume results to the parameter changes where the blue column denotes using the low estimate and red

denotes the high estimate as seen in table 6.1 above, while figures 6.20-6.23 shows the key parameters

affecting volume calculation in the NORNE field, the values used in the plot have been gotten by

calculating the amount of deviation from the base case for each variable. For STOIIP calculation the

two key parameters are Porosity and Sw, for GIIP it‟s So while the position of the contacts have the least

impact, in HCPVoil and HCPVgas the key parameters are porosity and Sw respectively.

Figure 6.16 STOIIP (106 Sm3) Fig 6.17 GIIP (106 Sm3)

0 10 20 30 40

PORO

So

Sw

GOC

OWC

STOIIP

Low

High

2 000 0 2 000 4 000

PORO

So

Sw

GOC

OWC

GIIP

Low

High

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Figure 6.18 HCPVoil (106 Rm3) Figure 6.19 HCPVgas (106 Rm3)

Figure 6.20 STOIIP Figure 6.21 GIIP

0 10 20 30 40 50

PORO

So

Sw

GOC

OWC

HCPVoil

Low

High

10 5 0 5 10 15

POROSITY

So

Sw

GOC

OWC

HCPVgas

Low

High

0 5 10 15 20

PORO

So

Sw

GOC

OWC

EFFECTS ESTIMATE (STOIIP)

0 1000 2000 3000 4000

PORO

So

Sw

GOC

OWC

EFFECTS ESTIMATE (GIIP)

89



Figure 6.22 HCPVoil Figure 6.23 HCPVgas

0 10 20 30

PORO

So

Sw

GOC

OWC

EFFECTS ESTIMATE (HCPVoil)

0 5 10 15

POROSITY

So

Sw

GOC

OWC

EFFECTS ESTIMATE (HCPVgas)

90



Chapter 7

7.1 Impact Of Grid Resolution On well Placement

7.1.1 Optimum Well Placement

Optimum well placement is a very challenging problem due to the large number of decision variables

involved and the nonlinearity of the reservoir response as well as of the well placement constraints. Over

the years, a lot of research has been done on this problem, most of which using optimization routines

coupled to reservoir simulation models. Despite all this research, there is still a lack of robust computer-

aided optimization tools ready to be applied by asset teams in real field development projects.

Development strategies and well placement may significantly depend on field geology, maturity of the

depletion stage, technological factor, drive resources and other parameters. Optimum well placement

most of the time is done based on a deterministic (most likely) case.

The definition of a well placement is a key aspect with major impact in a field development project. In

this sense, the use of reservoir simulation allows the engineer to evaluate different placement scenarios.

However, the current industry practice is still, in most cases, a manual procedure of trial and error that

requires a lot of experience and knowledge from the engineers involved in the project. Considering that,

the development of well placement optimization tools which can automate this process is a high

desirable goal.

Different studies have been performed in well placement optimization; in here a few of them are cited/ described:

The optimum placement of wells within geologic uncertainties using sector model[23]

Using an optimization methodology and workflow for applying uncertainty analysis. The

workflow steps to follow are:

o Identify the highest critical parameters among the list of uncertainties of the field (using

Tornado plots);

o Apply simulation runs (using eclipse) to build a proxy model;

o Generate a density distribution function of cumulative production to identify the P10, P50

and P90 cases (using Monte Carlo Analysis);

91



o Use the probabilistic distribution to determine the likelihood of the development case

under identified risk parameters;

o Consider field operation strategies together with optimization of injector-producer

spacing, producer-producer spacing, horizontal completion, well length and orientation

with geologic uncertainties;

o Use the optimized development plan and well placement to generate cumulative

production distribution with uncertainty parameters.

Well placement under time-dependent uncertainty[22]

Well placement decisions made during the early stages of exploration and development activities have

the capability to improve later placement decisions by providing more information. Therefore, recovery

and efficient use of information may add value beyond the amount of oil recovered. This study proposes

an approach that emphasizes the value of time dependent information to achieve better decisions in

terms of reduced uncertainty and increased probable net present value (NPV). Unlike previous

approaches, well-placement optimization is coupled with recursive probabilistic history-matching steps

through the use of the pseudo-history concept. The pseudo-history is defined as the probable (future)

response of the reservoir that is generated by a probabilistic forecasting model.

For greater understanding of the time dependent uncertainty and its effect on well placement a simple

scenario is considered where there are such number of wells, same number for producer and injector,

whereas the production history for a determined number of days is available, which will be used to

generate multiple history-matched models.

Then the framework will be: The starting time of production of the optimized wells and the field

deployment are predetermined. The decision criterion is the utility that is a function of NPV and risk

attitude of the decision maker, which is the optimum location of the wells considering the uncertainty of

geological models.(7)

92



Well Placement using Genetic Algorithms with nonlinear Constraints[21]

According to SPE Paper 118808 an implemented tool based on Genetic Algorithm was used for the

simultaneous optimization of number, location and trajectory of producer and injector wells. This tool is

to deal with realistic well placement problems with arbitrary well trajectories, complex model grids and

linear and non-linear constraints.

It uses a technique called Genocop III-Genetic Algorithm for Numerical Optimization of Constraint

Problems-to deal with well placement constraints. Such constraints include grid size, maximum length

of wells, and minimum distance between wells, inactive grid cells and user-defined regions of the

model, with non-uniform shape, where the optimization routine is not supposed to place wells.

The optimization process was applied to different full-field reservoir models based on real cases. It

increased the net present values and the oil recovery factors obtained by well placement scenarios

previously proposed by engineers.

Genetic Algorithm (GA) is a robust optimization technique based on analogies to natural selection and

genetics. GA combines concepts such as survival of the fittest individual and random crossing

information.

The Genocop III method uses two separate populations, but an evolution in one of them influences

evaluations of individuals in the other one. Using this procedure is possible to deal with the following

well placement constraints in the optimization process:

Grid size

Maximum length of wells

Minimum distance between wells

Inactive grid cells

User defined regions of the model, with non-uniform shape, where the optimization routine is not

supposed to place wells.

With this set of constraints the optimization algorithm becomes applicable to reservoir models with non-

uniform geometry. These constraints also allow some flexibility in the definition of the optimization

problem.(8)

93



7.1.2 Impact of Grid Resolution during Well Placement.

In reservoir simulation, grid sizes are sometimes too large to give accurate results especially in regions

near well and displacement fronts. A global grid refinement for the whole reservoir is also not practical

since it is time consuming especially in large and complex models. When placing wells in reservoir

models, using fine grid models it usually helps in locating the well optimally for oil and gas production.

The impact of grid resolution when placing horizontal wells has been studied in this project. The Norne

field has an impermeable layer located at zone 4 just below the gas zone, synthetic models of the Norne

E-segment with its grid resolution changed both vertically and laterally has been built with properties of

the E-segmented populated to them. A well of similar specifications has been placed in each of the

synthetic models to determine at which point in the reservoir the model should be placed to delay water

breakthrough, optimize oil production and reduce water cut in the well. Three synthetic models have

been built as detailed below in table 7.1 and shown in figures 7.1 to 7.3; the grid refinement in each

model has been done just below the shale zone which is our area of interest.

Table 7.1

Model Grid Active cells

Model 1 16 X 45 X 22 15840

Model 2 16 X 45 X 24 17280

Model 3 16 X 45 X 25 18000

Wells: Table 7.2 below shows the specifications of the 3 wells used in the analysis. Each of the wells

has been completed with a 6.625” diameter casings, 4” diameter tubings and 827m of perforations along

the horizontal section. A packer has been installed to isolate the annulus from the production conduit to

enable controlled production.

94



Table 7.2

Wells Measured Depth (MD) Horizontal Section

C1 2846M 1100m

C2 2835 1096m

C3 2800m 1070m

Figure 7.1 Model 1

95



Figure 7.1 Model 2

Figure 7.3 Model 3

96



Development Strategy: Similar development strategies have been employed in all the cases listed

above, the simulation has been run for a seven years period from 1997 till 2004. Well and production

limits have been imposed on the horizontal well „C1‟, „C2‟ and „C3‟. Oil production rate has been fixed

at 500 Sm3/day till a bottom hole pressure of 150 bars is reached after which the bottom hole pressure is

kept constant during the remaining production life of the wells.

7.1.2.1 Observation Analysis

After running various scenarios with the wells placed at different points in the models the cases giving

the best result for each model has been selected and can be seen in the figure below. Model 3 has the

longest production life and produces till 2004 while in models 1 and 2 the pressure quickly drops to 150

bars and is subsequently shut in. Figures 1, 2, 3 and 4 shows the FOPR, GPR, FPR and cumulative

production for each case; it can be observed that model 3 which has the finest grid resolution has its well

placed most optimally. There is a noticeable difference from the results gotten from the 3 models even

though the difference in number of layers added is small, this is probably due to that refinement of the

grid was done on only two layers to help locate the well optimally and before refinement each grid had a

height of about 9m which reduces considerable when refined.

97



Figure 7.1 Oil Production Rate for the 3 models

Figure 7.2 Gas Production Rate for the 3 models

98



Figure 7.3 Average Pressure for the 3 models

Figure 7.4 FOPT for the new models

99



Chapter 8

Conclusions

A PETREL model of the Norne E-segment is now available for further research using Petrel. Since the 3D grid was not built on Petrel to do studies concerning grid resolution a synthetic

model needs to be built. The well path in PETREL is different from that in ECLIPSE.

History Matching: Modeling the aquifer support for the E-Segment improves significantly the match in the

Reservoir model. The vertical permeability near the Oil-Water contact has been reduced to decrease the flow and

production of water into the wells. A better history match was achieved at a Well and field level when combining all the modified

parameters.

Prediction

The reservoir has a better predicted recovery if developed from a new producer after 2003 and the other producers are shut in while maintaining use of the former injectors.

Uncertainty/Sensitivity Assessment

Due to the complexity and heterogeneity of Reservoirs, uncertainty analysis should be done on reservoir models to determine the impact parameters have on simulation results.

A range on the degree of uncertainty should be established so as to use realistic variables while doing uncertainty assessment.

A good uncertainty and sensitivity assessment will help in field development as volume calculations of oil and gas in place can be estimated more accurately.

Uncertain parameters in Norne E-segment affects the results from simulation runs making prediction more difficult.

Uncertainty in parameters important for volume calculations in the Reservoir model makes estimation of STOIIP and GIIP difficult.

Porosity and Sw has the greatest impact when calculating the STOIIP in the reservoir model.

100



Impact Of Grid Resolution On Well Placement

In well placement, the grid resolution helps in determination of the most exact place for optimum oil recovery. Using fine grid models increases the success rate in well placement.

Highest recovery is achieved when the well is placed in model „C‟ which has the finest grid resolution vertically of the 3 study cases.

Recommendations Pressure history for the wells should be made available to improve the history match done on the

field. To appreciate more the functionalities of PETREL more geological and reservoir data of the

Norne E-segment is needed to enable a more representative model be built on PETREL. Students need a more extensive training on PETREL or more access to their industry supervisors. Phd students or student assistants with knowledge of PETREL should be made available and

assigned to work with Masters students using PETREL for the Msc. Project and thesis. Since seismic can also be done on the PETREL platform, incorporating 4-D seismic data for

history matching will help in better and more reliable matches.

101



Bibliography 1.Calvin C. Mattax and Robert L.Dalton, “Reservoir simulation”, Monograph Volume 13, Henry L.

Doherty Series.

2-“Norne field”: http://www.offshore-technology.com/projects/statoil

3.Y.El Ouair, M.Lygren, B.osdal, O.Husby and M.Springer, “Integrated Reservoir Management

approach: From time-lapse Acquisition to Reservoir Model Update at the Norne Field”, Statoil ASA,

IPTC 10894.

4. R.Rwechungura, E. Suwartadi, M. Dadashpour, J. Kleppe, and B.Foss, “The Norne Field Case-A

unique Comparative Case Study”, SPE 127538, 2010.

5. Ferid T. Al-Kasim, Synove Tevik, Knut arne Jakobsen, Statoil ASA, Yula Tang, “Remotely

Controlled In-Situ Gas Lift on the Norne Subsea Field”, SPE, Scandpower A/S, Younes Jalali, SPE

77660, Schlumberger,

6. D. J. Schiozer, S. L. Almeida Netto, E. L. Ligero, C. Maschio, “Integration of History Matching And

Uncertainty Analysis”, JCPT, Volume 44, Number 7, 2005 Canada.

7. U. Ozdogan, R. N. Horne,”Optimization of Well Placement Under Time-Dependent

Uncertainty”SPE-90091, 2006.

8. A. Emerick, E. Silva, B. Messer, L. Almeida, D. Szwarcman, M. Pacheco and M. Vellasco, “Well

Placement Optimization Using A Genetic Algorithm With Nonlinear Constraints”, SPE 1118808, 2009.

9. Petrel Online Help Manual 2009.2.

10. Eclipse Simulation Software Manuals 2009.2 “Eclipse Technical Description”.

11. Eclipse Simulation Software Manuals 2009.2 “Eclipse Reference Manual”.

12. http://www.ipt.ntnu.no/~kleppe/TPG4150/krpc.pdf

13. http://www.statoil.com/en/ouroperations/explorationprod/ncs/norne/pages/default.aspx

14. M.Tahir “Reservoir History Matching-A Manual Approach”, NTNU, Master Thesis 2008.

15. Chen, W.H et al.: “A new algoritm for Automatic History Matching”, SPEJ, Dec 1974

16. A. Sahni, J. Swain, J. Merrel, W. Abriel, C. Bluhm, S. Unser, T. Lim, M. Wei. “Uncertainty

Analysis in the Seismic to Reservoir Simulation Workflow for a New Offshore Heavy Oil

Development”.

http://www.offshore-technology.com/projects/statoil

http://www.ipt.ntnu.no/~kleppe/TPG4150/krpc.pdf

http://www.statoil.com/en/ouroperations/explorationprod/ncs/norne/pages/default.aspx

102



17. Fathe A., Zoltan H., Georg M.,”A New Computer Assisted History Matching Method” SPE 130426,

June 2010.

18. T.B. Tan and N.Kalogerakis, “Improved Reservoir Characterization Using Automatic History

Matching Procedures” JCPT93-06-02.

19. P.-H.Yang, A. T.Watson, ”Automatic History Matching with Variable-Metric Methods”,

20. E.M. El-M Shokir, M. Emera; S. M. Eid, and A. A Waly, “Optimal 3-D Directional & Horizontal

Wells Planning Using Genetic Algorithm”, SPE 79164, June 2002.

21. Alexandre A. Emerick, Eugenio Silva, Bruno Messer, Luciana F. Almeida, Dilza Szwarcman,

Marco Aurelio C. Pacheco, and Marley M.B.R. Vellasco,” Well Placement Optimization Using a

Genetic Algorithm With Nonlinear Constraints” SPE118808, June 2009.

22. Umut Ozdogan, SPE, Chevron Energy Technology Co., and Roland N. Horne, SPE, Stanford U,”

Optimization of Well Placement Under Time-Dependent Uncertainty”, SPE 90091-PA, 2006

23. Rabah Mesdour and Lee Ramsey, Schlumberger, and Ahmed Aly, AUC/Schlumberger, “Optimizing

Development Well Placements within Geological Uncertainty Utilizing Sector Model”, SPE 128471-

MS, 2010

103



Nomenclature So Oil Saturation

Sw Water Saturation

Sg Gas Saturation

Krw Relative Permeability of water

Kro Relative Permeability of Oil

SWCR Critical Water Saturation

STOIIP Stock Tank Oil initially in Place

GIIP Gas initially in place

HCPVoil Hydrocarbon Pore Volume (Oil)

HCPVgas Hydrocarbon Pore Volume (gas)

PERMZ Vertical Permeability

PERMX Horizontal Permeability

Poro Porosity

Bo Oil Formation volume factor

Bg Gas Formation volume factor

GOC Gas-Oil Contact

OWC Oil-Water Contact

FOPT Field Oil Production Total

FOIP Field Oil in Place

104



FGIP Field Gas in Place

FGPR Field Gas Production Rate

FOPR Field Oil Production Rate

WOPR Well Oil Production Rate

BHP Bottom Hole Pressure

GOR Gas-Oil Ratio

NTG Net to Gross

Rm3 Reservoir cubic metres

Sm3 Standard cubic metres

3D Three Dimensions

CDC Cumulative distribution curve

HM History Matched

105



List of figures Figure 2.1 The Norne Field 3 Figure 2.2 Map of Norne field. The 4 main fault blocks are denoted C. D, E and G 4 Figure 2.3. Old and new zonation of the Norne Field 5 Figure 2.4 Stratigraphical sub-division of the Norne reservoir (StatoilHydro, 2001) 6 Figure 2.5. The Norne Sub-Sea system 8 Figura 2.6 Development of the Norne Field 8 Figure 2.7 Cross-Section Area of the Norne field 11 Figure 2.8 Norne Field drainage pattern 12 Figure 3.1 The E-segment showing the oil saturation and the wells 13 Figure 3.2 Faults of the E-Segment 14 Figure 3.3 One of the horizons of the E-Segment 15 Figure 3.4 Well E-2H in Petrel and Eclipse respectively 18 Figure 3.5 Well E-3AH in Petrel and Eclipse respectively 19 Figure 3.6 Well E-3H in Petrel and Eclipse respectively 19 Figure 3.7 Norne Field as in Eclipse Model 20 Figure 3.8 Norne Field E-Segment as in Petrel Model 20 Figure 4.1 FOPR for the base case and the history 34 Figure 4.2 FWPR for the base case and the history 35 Figure 4.3 Relative Permeability curves (before (1) and after simulation (22 and 91)) for different values of SWCR 37 Figure 4.4 History Matching of FOPR after changing SWCR 38 Figure 4.5 History Matching of FGOR after changing SWCR 38 Figure 4.6 History Matching of FOPT after changing SWCR 39 Figure 4.7 History Matching of FGPR after changing SWCR 39 Figure 4.8 History Matching of FWCT after changing SWCR 40 Figure 4.9 The Norne E-Segment before adding the Aquifer 41 Figure 4.10 The Norne E-Segment after adding the Aquifer 42 Figure 4.11 History Matching of FOPR after adding an aquifer 42 Figure 4.12 History Matching of FGOR after adding an aquifer 43 Figure 4.13 History Matching of FOPT after adding an aquifer 43 Figure 4.14 History Matching of FGPR after adding an aquifer 44 Figure 4.15 History Matching of FWCT after adding an aquifer 44 Figure 4.16 History Matching of FOPR after changing PERMZ and PERMX 45 Figure 4.17 History Matching of FGOR after changing PERMZ and PERMX 46 Figure 4.18 History Matching of FOPT after changing PERMZ and PERMX 46 Figure 4.19 History Matching of FGPR after changing PERMZ and PERMX 47 Figure 4.20 History Matching of FWCT after changing PERMZ and PERMX 47 Figure 4.21 Typical curves for oil-water relative permeability at water wetted system 48 Figure 4.22 Oil/Water and Oil/Gas relative Permeability Curves 50 Figure 4.23 Relative Permeability curves before and after changing the maximum values of Kro and Krw 51 Figure 4.24 History Matching of FOPR after changing Krw and Kro 52 Figure 4.25 History Matching of FGOR after changing Krw and Kro 52 Figure 4.26 History Matching of FOPT after changing Krw and Kro 53 Figure 4.27 History Matching of FGPR after changing Krw and Kro 53 Figure 4.28 History Matching of FWTC after changing Krw and Kro 54 Figure 4.29 History Matching of FOPR after combining the changed parameters 54 Figure 4.30 History Matching of FGOR after combining the changed parameters 55

106



Figure 4.31 History Matching of FOPT after combining the changed parameters 55 Figure 4.32 History Matching of FGPR after combining the changed parameters 56 Figure 4.33 History Matching of FWCT after combining the changed parameters 56 Figure 4.34 History Matching of Well E-2H after combining the changed parameters 57 Figure 4.35 History Matching of Well E-3AH after combining the changed parameters 57 Figure 4.36 History Matching of Well E-3H after combining the changed parameters 58 Figure 5.1 A multi-lateral, multi-segmented well 62 Figure 5.2 Well Segments 63 Figure 5.3 Allocating connection flows to segments 64 Figure 5.4 FOPR with Current Development Strategy 66 Figure 5.5 FGPR with Current Development Strategy 66 Figure 5.6 FOPR with New Development Strategy 67 Figure 5.7 FOPR with New Development Strategy 67 Figure 5.8 FOPT with New Development Strategy 68 Figure 5.9 FOIP with New Development Strategy 68 Figure 5.10 FGIP with New Development Strategy 69 Figure 5.11 Field Average Pressure with and without Multisegmented option 70 Figure 5.12 FOPR with and without Multisegmented option 70 Figure5.13 Oil Rate at 01/12/1999 71 Figure 5.14 Gas, oil and water rates at 01/12/1999 71 Figure 5.15 Water, oil and gas velocities at 01/12/1999 72 Figure 5.16 Pressure at 01/12/1999 72 Figure 5.17 Oil hold up fraction 73 Figure 5.18 Gas holdup fraction 73 Figure 6.1 The basics of calculating volumes in Petrel 77 Figure 6.2 STOIIP (106 Sm3) 78 Figure.6 3 HCPVOil (106 Rm3) 78 Figure 6.4 HCPVgas (106 Rm3) 79 Figure 6.5 FOPR using all multipliers 80 Figure 6.6 FGPR using all multipliers 81 Figure 6.7 FGPR using 0.4 multiplier 81 Figure 6.8 FOPR using 0.4 multiplier 82 Figure 6.9 STOIIP Zone 1 83 Figure 6.10 STOIIP Zone 5 83 Figure 6.11 Porosity 84 Figure 6.12 So 84 Figure 6.13 Sw 85 Figure 6.14 GOC 85 Figure 6.15 OWC 86 Figure 6.16 STOIIP (106 Sm3) 87 Fig 6.17 GIIP (106 Sm3) 87 Figure 6.18 HCPVoil (106 Rm3) 88 Figure 6.19 HCPVgas (106 Rm3) 88 Figure 6.20 STOIIP 88 Figure 6.21 GIIP 88 Figure 6.22 HCPVoil 89 Figure 6.23 HCPVgas 89

107



Figure 7.1 Model 1 94 Figure 7.1 Model 2 95 Figure 7.3 Model 3 95 Figure 7.1 Oil Production Rate for the 3 models 97 Figure 7.2 Gas Production Rate for the 3 models 97 Figure 7.3 Average Pressure for the 3 models 98 Figure 7.4 FOPT for the new models 98 Figure 1.A Tables showing the change on the SWCR. 109 Figure 2A Table showing SWCR on the simulation run 110 Figure 3A Table showing Aquifer on the simulation run 110 Figure 4A Table showing the Krw and Kro on the simulation run 111 Figure 5A Table showing the SWCR, Krw, Kro and Aquifer on the simulation run 111 Figure 6A Table showing the property calculator used for different values of PermZ and PermX 112

List Of Tables Table 1. Active development wells in the Norne Field ( NPD fact pages 2009) 9 Table 2.2 Initial volumes in place Oil and Gas 10 Table 2.3 The NPDs reserve estimates as of 31.12.2009 10 Table 3.1 Faults on the E-Segment 15 Table 3.2 Oil and Gas Properties 17 Table 3.3 Water Reservoir Properties 17 Table 4.1 Wells data available 34 Table 4.2 Saturation table end points 36 Table 5.1 Development Strategy 65 Table 5.2 Prediction Strategy 65 Table 6.1 High and low ranges used on the parameters 86 Table 7.1 Synthetic models 93 Table 7.2 New Wells 94

108



Appendix A Tables with modified parameters performed in Petrel 2009.2

Figure 1A. Tables showing the change on the SWCR.

109



Tables with Simulation Runs

Figure 2A. Table showing SWCR on the simulation run

Figure 3A Table showing Aquifer on the simulation run

110



Figure 4A Table showing the Krw and Kro on the simulation run

Figure 5A Table showing the SWCR, Krw, Kro and Aquifer on the simulation run

111



Property Calculators

Figure 6A Table showing the property calculator used for different values of PERMZ and PERMX

112



The Producers on the E-Segment

Producer E-2H

Producer E-3AH

Producer E-3H

113



The Injectors on the E-Segment

Injector F-3H

Injector F-1H

114



Appendix B

Well Trajectory

E-2H Well Data Export Project : NORNE Cartographic System : ST_ED50_UTM32N_CM9NE Depth Mode : MD Depth Unit : International meters Distance Unit : meters Date : Thu Dec 27 14:52:43 2007 UWI: NO 6608/10-E-2 H Common: 6608/10-E-2 H Measr. Depth TVD Depth TVDSS Depth X-Offset Y-Offset 0.0000 0.0000 22.0000 0.0000 0.0000 402.0000 402.0000 -380.0000 0.0000 0.0000 430.0000 430.0000 -408.0000 -0.0107 -0.0059 460.0000 460.0000 -438.0000 -0.0472 -0.0199 490.0000 489.9998 -467.9998 -0.1225 -0.0694 520.0000 519.9996 -497.9996 -0.1628 -0.1600 550.0000 549.9993 -527.9993 -0.1879 -0.2671 580.0000 579.9984 -557.9984 -0.2802 -0.4773 610.0000 609.9893 -587.9893 -0.4322 -1.1680 640.0000 639.9553 -617.9553 -0.7422 -2.5484 670.0000 669.8770 -647.8770 -1.3458 -4.6139 700.0000 699.7119 -677.7119 -2.1576 -7.6323 730.0000 729.3710 -707.3710 -3.1430 -12.0081 760.0000 758.7671 -736.7671 -4.4638 -17.8355 790.0000 787.9462 -765.9462 -6.0890 -24.6108 820.0000 816.9830 -794.9830 -7.9026 -31.9286 830.0000 826.6204 -804.6204 -8.5497 -34.5171 840.0000 836.2258 -814.2258 -9.2462 -37.2099 850.0000 845.8044 -823.8044 -9.9994 -39.9812 860.0000 855.3611 -833.3611 -10.7888 -42.8180 870.0000 864.9017 -842.9017 -11.5910 -45.7049 880.0000 874.4280 -852.4280 -12.3895 -48.6393 890.0000 883.9384 -861.9384 -13.1772 -51.6282 900.0000 893.4301 -871.4301 -13.9502 -54.6791 910.0000 902.8946 -880.8946 -14.6998 -57.8192 920.0000 912.3186 -890.3186 -15.4151 -61.0863 930.0000 921.7020 -899.7020 -16.1100 -64.4728 940.0000 931.0518 -909.0518 -16.7905 -67.9538 950.0000 940.3687 -918.3687 -17.4583 -71.5244 960.0000 949.6535 -927.6535 -18.1274 -75.1773 970.0000 958.8979 -936.8979 -18.8138 -78.9281 980.0000 968.0936 -946.0936 -19.5422 -82.7889 990.0000 977.2357 -955.2357 -20.3292 -86.7639 1000.0000 986.3097 -964.3097 -21.1784 -90.8796 1010.0000 995.3177 -973.3177 -22.0746 -95.1281 1020.0000 1004.2864 -982.2864 -22.9945 -99.4543 1030.0000 1013.2385 -991.2385 -23.9196 -103.8137 1040.0000 1022.1848 -1000.1848 -24.8428 -108.1855 1050.0000 1031.1337 -1009.1337 -25.7646 -112.5520

1060.0000 1040.0916 -1018.0916 -26.6849 -116.9005 1070.0000 1049.0610 -1027.0610 -27.6049 -121.2250 1080.0000 1058.0386 -1036.0386 -28.5196 -125.5339 1090.0000 1067.0211 -1045.0211 -29.4232 -129.8347 1100.0000 1076.0114 -1054.0114 -30.3224 -134.1204 1110.0000 1085.0039 -1063.0039 -31.2169 -138.4022 1120.0000 1093.9972 -1071.9972 -32.1074 -142.6833 1130.0000 1102.9966 -1080.9966 -32.9957 -146.9520 1140.0000 1112.0009 -1090.0009 -33.8793 -151.2113 1150.0000 1121.0098 -1099.0098 -34.7588 -155.4618 1160.0000 1130.0247 -1108.0247 -35.6272 -159.7017 1170.0000 1139.0453 -1117.0453 -36.4847 -163.9318 1180.0000 1148.0670 -1126.0670 -37.3355 -168.1608 1190.0000 1157.0868 -1135.0868 -38.1875 -172.3936 1200.0000 1166.1127 -1144.1127 -39.0490 -176.6116 1210.0000 1175.1498 -1153.1498 -39.9169 -180.8042 1220.0000 1184.1925 -1162.1925 -40.7831 -184.9851 1230.0000 1193.2329 -1171.2329 -41.6543 -189.1697 1240.0000 1202.2572 -1180.2572 -42.5316 -193.3877 1250.0000 1211.2394 -1189.2394 -43.4069 -197.6951 1260.0000 1220.1641 -1198.1641 -44.2729 -202.1222 1270.0000 1229.0482 -1207.0482 -45.1267 -206.6325 1280.0000 1237.9147 -1215.9147 -45.9766 -211.1782 1290.0000 1246.7743 -1224.7743 -46.8221 -215.7380 1300.0000 1255.6383 -1233.6383 -47.6605 -220.2906 1310.0000 1264.5112 -1242.5112 -48.4955 -224.8264 1320.0000 1273.3954 -1251.3954 -49.3292 -229.3404 1330.0000 1282.2975 -1260.2975 -50.1504 -233.8212 1340.0000 1291.2190 -1269.2190 -50.9581 -238.2658 1350.0000 1300.1492 -1278.1492 -51.7678 -242.6926 1360.0000 1309.0836 -1287.0836 -52.5698 -247.1120 1370.0000 1318.0303 -1296.0303 -53.3582 -251.5093 1380.0000 1326.9851 -1304.9851 -54.1369 -255.8917 1390.0000 1335.9469 -1313.9469 -54.9077 -260.2612 1400.0000 1344.9171 -1322.9171 -55.6786 -264.6132 1410.0000 1353.8970 -1331.8970 -56.4480 -268.9456 1420.0000 1362.8917 -1340.8917 -57.2090 -273.2484 1430.0000 1371.8994 -1349.8994 -57.9684 -277.5244 1440.0000 1380.9181 -1358.9181 -58.7337 -281.7761

115



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117



Well Data Export Project : NORNE Cartographic System : ST_ED50_UTM32N_CM9NE Depth Mode : MD Depth Unit : International meters Distance Unit : meters Date : Thu Dec 27 14:52:58 2007 PLG1 UWI: NO 6608/10-E-3 AH PLG2 Common: 6608/10-E-3 AH PLG3 Measr. Depth TVD Depth TVDSS Depth X-Offset Y-Offset PLG4 0.0000 0.0000 22.0000 0.0000 0.0000 PLG4 401.9999 401.9999 -379.9999 0.0000 0.0000 PLG4 402.0000 402.0000 -380.0000 -0.0000 0.0000 PLG4 420.0000 420.0000 -398.0000 -0.0157 0.0235 PLG4 450.0000 449.9999 -427.9999 -0.0406 0.0687 PLG4 480.0000 479.9987 -457.9987 0.0914 -0.1150 PLG4 510.0000 509.9937 -487.9937 0.3496 -0.5946 PLG4 540.0000 539.9883 -517.9883 0.6159 -1.0971 PLG4 570.0000 569.9836 -547.9836 0.8963 -1.5422 PLG4 600.0000 599.9491 -577.9491 1.3160 -2.8301 PLG4 630.0000 629.8486 -607.8486 1.7621 -5.2363 PLG4 660.0000 659.7050 -637.7050 1.7369 -8.1580 PLG4 690.0000 689.4881 -667.4881 0.9033 -11.6454 PLG4 720.0000 719.1327 -697.1327 -0.7318 -15.9361 PLG4 750.0000 748.5327 -726.5327 -3.1202 -21.3872 PLG4 780.0000 777.6568 -755.6568 -6.0481 -27.9553 PLG4 810.0000 806.6356 -784.6356 -9.3531 -34.9757 PLG4 820.0000 816.2698 -794.2698 -10.5545 -37.3712 PLG4 830.0000 825.8817 -803.8817 -11.7885 -39.8387 PLG4 840.0000 835.4792 -813.4792 -13.0544 -42.3456 PLG4 850.0000 845.0490 -823.0490 -14.3846 -44.9238 PLG4 860.0000 854.5811 -832.5811 -15.7953 -47.5972 PLG4 870.0000 864.0767 -842.0767 -17.2910 -50.3532 PLG4 880.0000 873.5122 -851.5122 -18.9299 -53.2306 PLG4 890.0000 882.8733 -860.8733 -20.7230 -56.2558 PLG4 900.0000 892.1648 -870.1648 -22.6174 -59.4301 PLG4 910.0000 901.3846 -879.3846 -24.5816 -62.7667 PLG4 920.0000 910.5231 -888.5231 -26.6132 -66.2820 PLG4 930.0000 919.5716 -897.5716 -28.7115 -69.9857 PLG4 940.0000 928.5383 -906.5383 -30.8460 -73.8638 PLG4 950.0000 937.4384 -915.4384 -32.9821 -77.8916 PLG4 960.0000 946.2761 -924.2761 -35.0818 -82.0729 PLG4 970.0000 955.0489 -933.0489 -37.1213 -86.4176 PLG4 980.0000 963.7563 -941.7563 -39.1241 -90.9085 PLG4 990.0000 972.3946 -950.3946 -41.1343 -95.5277 PLG4 1000.0000 980.9617 -958.9617 -43.1807 -100.2622 PLG4 1010.0000 989.4560 -967.4560 -45.2666 -105.1094 PLG4 1020.0000 997.8696 -975.8696 -47.4005 -110.0748 PLG4 1030.0000 1006.1983 -984.1983 -49.5951 -115.1557 PLG4 1040.0000 1014.4440 -992.4440 -51.8506 -120.3442 PLG4 1050.0000 1022.6174 -1000.6174 -54.1623 -125.6215 PLG4 1060.0000 1030.7234 -1008.7234 -56.5410 -130.9725 PLG4 1070.0000 1038.7661 -1016.7661 -58.9970 -136.3838 PLG4 1080.0000 1046.7629 -1024.7629 -61.5174 -141.8333 PLG4 1090.0000 1054.7289 -1032.7289 -64.1182 -147.2904 PLG4 1100.0000 1062.6758 -1040.6758 -66.8163 -152.7280 PLG4 1110.0000 1070.6083 -1048.6083 -69.5906 -158.1481

PLG4 1120.0000 1078.5365 -1056.5365 -72.4235 -163.5442 PLG4 1130.0000 1086.4807 -1064.4807 -75.2983 -168.8945 PLG4 1140.0000 1094.4354 -1072.4354 -78.2037 -174.2124 PLG4 1150.0000 1102.3779 -1080.3779 -81.1396 -179.5318 PLG4 1160.0000 1110.2881 -1088.2881 -84.1277 -184.8704 PLG4 1170.0000 1118.1477 -1096.1477 -87.1995 -190.2359 PLG4 1180.0000 1125.9493 -1103.9493 -90.3757 -195.6253 PLG4 1190.0000 1133.7006 -1111.7006 -93.6846 -201.0075 PLG4 1200.0000 1141.4186 -1119.4186 -97.1292 -206.3522 PLG4 1210.0000 1149.1097 -1127.1097 -100.6650 -211.6760 PLG4 1220.0000 1156.7460 -1134.7460 -104.2967 -217.0142 PLG4 1230.0000 1164.2833 -1142.2833 -108.0692 -222.3951 PLG4 1240.0000 1171.7007 -1149.7007 -111.9807 -227.8430 PLG4 1250.0000 1178.9980 -1156.9980 -116.0039 -233.3711 PLG4 1260.0000 1186.1731 -1164.1731 -120.1182 -238.9914 PLG4 1270.0000 1193.2230 -1171.2230 -124.3122 -244.7103 PLG4 1280.0000 1200.1234 -1178.1234 -128.5932 -250.5457 PLG4 1290.0000 1206.8268 -1184.8268 -132.9765 -256.5327 PLG4 1300.0000 1213.2568 -1191.2568 -137.4910 -262.7180 PLG4 1310.0000 1219.3621 -1197.3621 -142.1433 -269.1265 PLG4 1320.0000 1225.1495 -1203.1495 -146.9143 -275.7394 PLG4 1330.0000 1230.6439 -1208.6439 -151.7956 -282.5200 PLG4 1340.0000 1235.9222 -1213.9222 -156.7651 -289.4077 PLG4 1350.0000 1241.0831 -1219.0831 -161.7785 -296.3525 PLG4 1360.0000 1246.1984 -1224.1984 -166.8212 -303.3099 PLG4 1370.0000 1251.2775 -1229.2775 -171.8942 -310.2718 PLG4 1380.0000 1256.2159 -1234.2159 -177.0180 -317.2969 PLG4 1390.0000 1260.8663 -1238.8663 -182.2562 -324.4328 PLG4 1400.0000 1265.1854 -1243.1854 -187.6361 -331.6710 PLG4 1410.0000 1269.2583 -1247.2583 -193.1465 -338.9541 PLG4 1420.0000 1273.1865 -1251.1865 -198.7645 -346.2346 PLG4 1430.0000 1277.0375 -1255.0375 -204.4514 -353.5029 PLG4 1440.0000 1280.8546 -1258.8546 -210.1862 -360.7514 PLG4 1450.0000 1284.7209 -1262.7209 -215.9328 -367.9644 PLG4 1460.0000 1288.6908 -1266.6908 -221.6458 -375.1478 PLG4 1470.0000 1292.8138 -1270.8138 -227.3164 -382.2780 PLG4 1480.0000 1297.0321 -1275.0321 -232.9643 -389.3708 PLG4 1490.0000 1301.2345 -1279.2345 -238.6087 -396.4757 PLG4 1500.0000 1305.4821 -1283.4821 -244.2308 -403.5715 PLG4 1510.0000 1309.7990 -1287.7990 -249.8250 -410.6474 PLG4 1520.0000 1314.1229 -1292.1229 -255.4134 -417.7236 PLG4 1530.0000 1318.3997 -1296.3997 -261.0114 -424.8208 PLG4 1540.0000 1322.6195 -1300.6195 -266.6049 -431.9557 PLG4 1550.0000 1326.7894 -1304.7894 -272.1768 -439.1365 PLG4 1560.0000 1330.9189 -1308.9189 -277.7212 -446.3620 PLG4 1570.0000 1335.0126 -1313.0126 -283.2367 -453.6298 PLG4 1580.0000 1339.0457 -1317.0457 -288.7405 -460.9402 PLG4 1590.0000 1343.0051 -1321.0051 -294.2520 -468.2849 PLG4 1600.0000 1346.8932 -1324.8932 -299.7806 -475.6550 PLG4 1610.0000 1350.7014 -1328.7014 -305.3349 -483.0473

118



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PLG4 2320.0000 1582.2428 -1560.2428 -719.9782 -1010.5119 PLG4 2330.0000 1585.6892 -1563.6892 -726.0323 -1017.6861 PLG4 2340.0000 1589.1929 -1567.1929 -732.0621 -1024.8530 PLG4 2350.0000 1592.7587 -1570.7587 -738.0712 -1032.0067 PLG4 2360.0000 1596.3651 -1574.3651 -744.0703 -1039.1484 PLG4 2370.0000 1600.0073 -1578.0073 -750.0416 -1046.2952 PLG4 2380.0000 1603.6910 -1581.6910 -755.9720 -1053.4548 PLG4 2390.0000 1607.4128 -1585.4128 -761.8845 -1060.6095 PLG4 2400.0000 1611.2058 -1589.2058 -767.7930 -1067.7301 PLG4 2410.0000 1615.1115 -1593.1115 -773.7132 -1074.7795 PLG4 2420.0000 1619.2725 -1597.2725 -779.6454 -1081.6700 PLG4 2430.0000 1623.7880 -1601.7880 -785.5521 -1088.3564 PLG4 2440.0000 1628.6442 -1606.6442 -791.4174 -1094.8373 PLG4 2450.0000 1633.8480 -1611.8480 -797.2292 -1101.0925 PLG4 2460.0000 1639.3591 -1617.3591 -802.9603 -1107.1566 PLG4 2470.0000 1645.0339 -1623.0339 -808.6395 -1113.1183 PLG4 2480.0000 1650.7982 -1628.7982 -814.3014 -1119.0103 PLG4 2490.0000 1656.6887 -1634.6887 -819.9429 -1124.7958 PLG4 2500.0000 1662.6643 -1640.6643 -825.5779 -1130.5001 PLG4 2510.0000 1668.7097 -1646.7097 -831.2502 -1136.0924 PLG4 2520.0000 1674.9301 -1652.9301 -836.9484 -1141.4617 PLG4 2530.0000 1681.3505 -1659.3505 -842.6153 -1146.6250 PLG4 2540.0000 1687.8635 -1665.8635 -848.2535 -1151.7035 PLG4 2550.0000 1694.4739 -1672.4739 -853.8399 -1156.7125 PLG4 2560.0000 1701.2394 -1679.2394 -859.3337 -1161.6160 PLG4 2570.0000 1708.1122 -1686.1122 -864.7496 -1166.4567 PLG4 2580.0000 1715.0693 -1693.0693 -870.1056 -1171.2430 PLG4 2590.0000 1722.1447 -1700.1447 -875.4185 -1175.9022 PLG4 2600.0000 1729.3107 -1707.3107 -880.7145 -1180.4408 PLG4 2610.0000 1736.5222 -1714.5222 -886.0162 -1184.8999 PLG4 2620.0000 1743.7711 -1721.7711 -891.3243 -1189.2904 PLG4 2630.0000 1751.0656 -1729.0656 -896.6044 -1193.6390 PLG4 2640.0000 1758.4805 -1736.4805 -901.8124 -1197.8687 PLG4 2650.0000 1766.0132 -1744.0132 -906.9468 -1201.9791 PLG4 2660.0000 1773.5865 -1751.5865 -912.0321 -1206.0758 PLG4 2670.0000 1781.1793 -1759.1793 -917.0720 -1210.1927 PLG4 2680.0000 1788.8262 -1766.8262 -922.0397 -1214.2971 PLG4 2690.0000 1796.5751 -1774.5751 -926.9434 -1218.2852 PLG4 2700.0000 1804.4581 -1782.4581 -931.8278 -1222.0255 PLG4 2710.0000 1812.4561 -1790.4561 -936.7101 -1225.5172 PLG4 2720.0000 1820.5267 -1798.5267 -941.5932 -1228.8363 PLG4 2730.0000 1828.7133 -1806.7133 -946.4371 -1231.9196 PLG4 2740.0000 1837.0042 -1815.0042 -951.2290 -1234.8002 PLG4 2750.0000 1845.3584 -1823.3584 -955.9874 -1237.5499 PLG4 2760.0000 1853.8025 -1831.8025 -960.6757 -1240.1409 PLG4 2770.0000 1862.3134 -1840.3134 -965.2943 -1242.6377 PLG4 2780.0000 1870.8977 -1848.8977 -969.8515 -1244.9902 PLG4 2790.0000 1879.5806 -1857.5806 -974.3299 -1247.1227 PLG4 2800.0000 1888.3158 -1866.3158 -978.7693 -1249.1194 PLG4 2810.0000 1897.1045 -1875.1045 -983.1728 -1250.9528 PLG4 2820.0000 1905.9646 -1883.9646 -987.5129 -1252.5836 PLG4 2830.0000 1914.8790 -1892.8790 -991.7969 -1254.0591 PLG4 2840.0000 1923.8694 -1901.8694 -995.9875 -1255.3250 PLG4 2850.0000 1932.9288 -1910.9288 -1000.0821 -1256.4022 PLG4 2860.0000 1942.0255 -1920.0255 -1004.1188 -1257.3784 PLG4 2870.0000 1951.1729 -1929.1729 -1008.0750 -1258.1970 PLG4 2880.0000 1960.3605 -1938.3605 -1011.9619 -1258.8901 PLG4 2890.0000 1969.5676 -1947.5676 -1015.8113 -1259.5311 PLG4 2900.0000 1978.8031 -1956.8031 -1019.6139 -1260.0231 PLG4 2910.0000 1988.0643 -1966.0643 -1023.3741 -1260.3202 PLG4 2920.0000 1997.3345 -1975.3345 -1027.1219 -1260.4530 PLG4 2930.0000 2006.5956 -1984.5956 -1030.8922 -1260.3524 PLG4 2940.0000 2015.8456 -1993.8456 -1034.6776 -1260.0251 PLG4 2950.0000 2025.0841 -2003.0841 -1038.4700 -1259.5114 PLG4 2960.0000 2034.2960 -2012.2960 -1042.2784 -1258.7202 PLG4 2970.0000 2043.4836 -2021.4836 -1046.0962 -1257.7146 PLG4 2980.0000 2052.6655 -2030.6655 -1049.8905 -1256.5776 PLG4 2990.0000 2061.8438 -2039.8438 -1053.6517 -1255.3085 PLG4 3000.0000 2071.0544 -2049.0544 -1057.2936 -1253.9316 PLG4 3010.0000 2080.3489 -2058.3489 -1060.6815 -1252.4733 PLG4 3020.0000 2089.7224 -2067.7224 -1063.8335 -1250.9912 PLG4 3030.0000 2099.1387 -2077.1387 -1066.8303 -1249.4584

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PLG4 3040.0000 2108.5820 -2086.5820 -1069.6381 -1247.7466 PLG4 3050.0000 2118.0354 -2096.0354 -1072.3154 -1245.8856 PLG4 3060.0000 2127.4766 -2105.4766 -1074.9805 -1243.9468 PLG4 3070.0000 2136.8777 -2114.8777 -1077.6125 -1241.7826 PLG4 3080.0000 2146.2344 -2124.2344 -1080.2308 -1239.4165 PLG4 3090.0000 2155.5732 -2133.5732 -1082.8506 -1236.9833 PLG4 3100.0000 2164.8979 -2142.8979 -1085.3914 -1234.4160 PLG4 3110.0000 2174.1880 -2152.1880 -1087.8230 -1231.6272 PLG4 3120.0000 2183.4160 -2161.4160 -1090.1808 -1228.5812 PLG4 3130.0000 2192.5764 -2170.5764 -1092.4412 -1225.2693 PLG4 3140.0000 2201.6973 -2179.6973 -1094.5973 -1221.7825 PLG4 3150.0000 2210.7891 -2188.7891 -1096.6606 -1218.1660 PLG4 3160.0000 2219.8411 -2197.8411 -1098.6604 -1214.4161 PLG4 3170.0000 2228.8679 -2206.8679 -1100.6559 -1210.6041 PLG4 3180.0000 2237.8840 -2215.8840 -1102.6509 -1206.7661 PLG4 3190.0000 2246.8894 -2224.8894 -1104.6205 -1202.8904 PLG4 3200.0000 2255.8840 -2233.8840 -1106.5275 -1198.9584 PLG4 3210.0000 2264.8655 -2242.8655 -1108.2786 -1194.9260 PLG4 3220.0000 2273.8132 -2251.8132 -1109.8370 -1190.7421 PLG4 3230.0000 2282.7268 -2260.7268 -1111.2751 -1186.4435 PLG4 3240.0000 2291.5894 -2269.5894 -1112.5645 -1181.9955 PLG4 3250.0000 2300.3562 -2278.3562 -1113.7074 -1177.3230 PLG4 3260.0000 2309.0381 -2287.0381 -1114.8146 -1172.4860 PLG4 3270.0000 2317.6731 -2295.6731 -1115.8754 -1167.5553 PLG4 3280.0000 2326.2671 -2304.2671 -1116.8624 -1162.5387 PLG4 3290.0000 2334.7993 -2312.7993 -1117.8203 -1157.4121 PLG4 3300.0000 2343.2341 -2321.2341 -1118.7612 -1152.1238 PLG4 3310.0000 2351.5740 -2329.5740 -1119.6256 -1146.6744 PLG4 3320.0000 2359.8391 -2337.8391 -1120.3293 -1141.0900

PLG4 3330.0000 2368.0034 -2346.0034 -1120.8060 -1135.3359 PLG4 3343.4299 2378.7900 -2356.7900 -1121.1741 -1127.3445 PLG4 3371.4700 2400.5359 -2378.5359 -1120.7041 -1109.6663 PLG4 3399.8401 2421.5574 -2399.5574 -1118.4253 -1090.7589 PLG4 3428.4099 2442.0510 -2420.0510 -1114.8357 -1071.1827 PLG4 3457.1001 2462.1458 -2440.1458 -1110.6145 -1051.1455 PLG4 3485.3000 2481.3616 -2459.3616 -1106.0079 -1031.0294 PLG4 3513.4900 2499.7070 -2477.7070 -1100.7863 -1010.2764 PLG4 3541.5701 2516.9741 -2494.9741 -1095.3418 -988.8157 PLG4 3570.1899 2533.5498 -2511.5498 -1089.6418 -966.1945 PLG4 3598.8899 2548.9456 -2526.9456 -1083.9886 -942.6481 PLG4 3627.2300 2562.7698 -2540.7698 -1078.4952 -918.5303 PLG4 3655.7700 2575.4634 -2553.4634 -1072.6158 -893.6564 PLG4 3670.8501 2581.7336 -2559.7336 -1069.4133 -880.3212 PLG4 3703.7800 2594.4106 -2572.4106 -1061.0577 -851.1144 PLG4 3731.7700 2603.9751 -2581.9751 -1052.1659 -826.3625 PLG4 3759.3899 2611.9875 -2589.9875 -1041.8510 -802.0370 PLG4 3788.3701 2618.8213 -2596.8213 -1030.2579 -776.3748 PLG4 3816.8101 2624.0032 -2602.0032 -1019.0623 -750.7546 PLG4 3844.5801 2627.3992 -2605.3992 -1007.7673 -725.6192 PLG4 3873.5901 2629.5161 -2607.5161 -995.7563 -699.2995 PLG4 3902.1101 2630.9341 -2608.9341 -983.9644 -673.3704 PLG4 3931.2000 2632.4312 -2610.4312 -971.7628 -647.0059 PLG4 3960.1399 2633.9734 -2611.9734 -959.4150 -620.8782 PLG4 3988.9299 2635.6331 -2613.6331 -946.9757 -594.9677 PLG4 4017.6799 2637.4758 -2615.4758 -934.6263 -569.0707 PLG4 4047.2800 2639.6951 -2617.6951 -921.9752 -542.4035 PLG4 4076.9500 2642.3069 -2620.3069 -909.5201 -515.6018 PLG4 4104.7402 2643.9236 -2621.9236 -897.5496 -490.5805 PLG4 4133.4702 2644.1316 -2622.1316 -884.3630 -465.0594 PLG4 4189.0000 2643.3562 -2621.3562 -858.3473 -416.0070

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Well Data Export Project : NORNE Cartographic System : ST_ED50_UTM32N_CM9NE Depth Mode : MD Depth Unit : International meters Distance Unit : meters Date : Thu Dec 27 14:53:48 2007 PLG1 UWI: NO 6608/10-E-3 H PLG2 Common: 6608/10-E-3 H PLG3 Measr. Depth TVD Depth TVDSS Depth X-Offset Y-Offset PLG4 0.0000 0.0000 22.0000 0.0000 0.0000 PLG4 402.0000 402.0000 -380.0000 0.0000 0.0000 PLG4 420.0000 420.0000 -398.0000 -0.0140 0.0209 PLG4 450.0000 449.9999 -427.9999 -0.0329 0.0693 PLG4 480.0000 479.9986 -457.9986 0.1323 0.1354 PLG4 510.0000 509.9936 -487.9936 0.3977 -0.3421 PLG4 540.0000 539.9882 -517.9882 0.6685 -0.8437 PLG4 570.0000 569.9838 -547.9838 0.9549 -1.2662 PLG4 600.0000 599.9797 -577.9797 1.2345 -1.6775 PLG4 630.0000 629.9760 -607.9760 1.5026 -2.0682 PLG4 660.0000 659.9730 -637.9730 1.7361 -2.4190 PLG4 690.0000 689.9705 -667.9705 1.9168 -2.7556 PLG4 720.0000 719.9464 -697.9464 1.9579 -3.8486 PLG4 750.0000 749.7790 -727.7790 0.6702 -6.6495 PLG4 780.0000 779.2827 -757.2827 -1.9903 -11.3443 PLG4 810.0000 808.5440 -786.5440 -5.3222 -17.0598 PLG4 840.0000 837.7567 -815.7567 -8.8007 -22.9347 PLG4 870.0000 866.9321 -844.9321 -12.3928 -28.9260 PLG4 900.0000 896.0317 -874.0317 -16.1697 -35.1649 PLG4 930.0000 924.9478 -902.9478 -20.3623 -41.9625 PLG4 960.0000 953.6944 -931.6944 -24.9176 -49.2341 PLG4 990.0000 982.3727 -960.3727 -29.5985 -56.6932 PLG4 1020.0000 1010.9857 -988.9857 -34.3523 -64.3543 PLG4 1050.0000 1039.5294 -1017.5294 -39.0687 -72.2914 PLG4 1080.0000 1068.0072 -1046.0072 -43.5804 -80.5766 PLG4 1110.0000 1096.3844 -1074.3844 -48.0191 -89.2377 PLG4 1140.0000 1124.6221 -1102.6221 -52.6754 -98.2341 PLG4 1170.0000 1152.8021 -1130.8021 -57.5498 -107.2963 PLG4 1200.0000 1181.0143 -1159.0143 -62.4972 -116.2179 PLG4 1230.0000 1209.2584 -1187.2584 -67.4716 -125.0227 PLG4 1260.0000 1237.5140 -1215.5140 -72.4769 -133.7731 PLG4 1290.0000 1265.7837 -1243.7837 -77.4832 -142.4774 PLG4 1320.0000 1294.0900 -1272.0900 -82.4076 -151.1092 PLG4 1350.0000 1322.4366 -1300.4366 -87.2156 -159.6733 PLG4 1380.0000 1350.8259 -1328.8259 -91.8585 -168.1872 PLG4 1410.0000 1379.2339 -1357.2339 -96.3410 -176.7252 PLG4 1440.0000 1407.6418 -1385.6418 -100.7623 -185.2950

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Well Data Export Project : NORNE Cartographic System : ST_ED50_UTM32N_CM9NE Depth Mode : MD Depth Unit : International meters Distance Unit : meters Date : Thu Dec 27 14:55:04 2007 PLG1 UWI: NO 6608/10-F-1 H PLG2 Common: 6608/10-F-1 H PLG3 Measr. Depth TVD Depth TVDSS Depth X-Offset Y-Offset PLG4 0.0000 0.0000 24.0000 0.0000 0.0000 PLG4 403.0000 403.0000 -379.0000 0.0000 0.0000 PLG4 464.0000 464.0000 -440.0000 0.0000 0.0000 PLG4 530.1000 530.0955 -506.0955 0.5214 0.4100 PLG4 555.8000 555.7902 -531.7902 1.0042 0.5826 PLG4 585.8000 585.7821 -561.7821 1.6990 0.6272 PLG4 614.8900 614.8607 -590.8607 2.4509 0.9186 PLG4 644.1400 644.0836 -620.0836 3.1920 1.9010 PLG4 673.1900 673.0521 -649.0521 3.6525 3.9864 PLG4 702.4500 702.1158 -678.1158 4.1133 7.3195 PLG4 731.5100 730.7850 -706.7850 4.5128 12.0254 PLG4 760.7700 759.5291 -735.5291 4.8338 17.4849 PLG4 790.0300 788.2729 -764.2729 5.5055 22.9145 PLG4 819.3600 816.9661 -792.9661 6.5728 28.8906 PLG4 848.3900 845.0845 -821.0845 7.8537 35.9809 PLG4 877.6300 872.9422 -848.9422 9.0431 44.7674 PLG4 906.8000 900.2023 -876.2023 10.1325 55.0840 PLG4 935.8000 926.8280 -902.8280 11.0096 66.5369 PLG4 965.1800 953.2988 -929.2988 11.8999 79.2471 PLG4 994.6600 979.3412 -955.3412 13.0428 93.0117 PLG4 1024.0400 1005.0064 -981.0064 14.2569 107.2598 PLG4 1053.3000 1030.5469 -1006.5469 15.4256 121.4888 PLG4 1082.6700 1056.1646 -1032.1646 16.5866 135.8060 PLG4 1112.0400 1081.6940 -1057.6940 17.8283 150.2732 PLG4 1141.3800 1107.3501 -1083.3501 18.8187 164.4696 PLG4 1170.7000 1133.1913 -1109.1913 19.4061 178.3095 PLG4 1200.1100 1159.1371 -1135.1371 19.8291 192.1509 PLG4 1229.4900 1185.0552 -1161.0552 20.2505 205.9805 PLG4 1258.8300 1210.7928 -1186.7928 20.6214 220.0604 PLG4 1288.2100 1236.3308 -1212.3308 20.8706 234.5835 PLG4 1317.5100 1261.6747 -1237.6747 21.0168 249.2858 PLG4 1330.0000 1272.4613 -1248.4613 21.0900 255.5821 PLG4 1352.5900 1291.8571 -1267.8571 21.1978 267.1617 PLG4 1381.9301 1316.9460 -1292.9460 21.4349 282.3706 PLG4 1411.2500 1341.9921 -1317.9921 21.8109 297.6089 PLG4 1440.6300 1367.0293 -1343.0293 22.0294 312.9801 PLG4 1469.9301 1391.9916 -1367.9916 22.1660 328.3214 PLG4 1499.1300 1416.8755 -1392.8755 22.4074 343.5978 PLG4 1528.3700 1441.7880 -1417.7880 22.7493 358.9023 PLG4 1557.6500 1466.7600 -1442.7600 23.1799 374.1838 PLG4 1586.8300 1491.7407 -1467.7407 23.5638 389.2594 PLG4 1616.1801 1516.9735 -1492.9735 23.8149 404.2484 PLG4 1645.5800 1542.3597 -1518.3597 24.1341 419.0734 PLG4 1674.9500 1567.8293 -1543.8293 24.5042 433.6937 PLG4 1704.3199 1593.3651 -1569.3651 24.8334 448.1992 PLG4 1733.3800 1618.6803 -1594.6803 25.0056 462.4679 PLG4 1762.7300 1644.3701 -1620.3701 24.9721 476.6608 PLG4 1792.0601 1670.2175 -1646.2175 24.8451 490.5224 PLG4 1821.3900 1696.1455 -1672.1455 24.7003 504.2324 PLG4 1850.7200 1722.0520 -1698.0520 24.4853 517.9821 PLG4 1880.0800 1747.9933 -1723.9933 24.2464 531.7297 PLG4 1909.3000 1773.8278 -1749.8278 23.9663 545.3793

PLG4 1938.2800 1799.4806 -1775.4806 23.6928 558.8586 PLG4 1967.4700 1825.5801 -1801.5801 23.3347 571.9222 PLG4 1996.6700 1852.1265 -1828.1265 23.0069 584.0760 PLG4 2025.9600 1879.1912 -1855.1912 22.9032 595.2694 PLG4 2055.2000 1906.6002 -1882.6002 22.8406 605.4490 PLG4 2084.3601 1934.2446 -1910.2446 22.6896 614.7237 PLG4 2113.3999 1962.0566 -1938.0566 22.4523 623.0696 PLG4 2142.5400 1990.2822 -1966.2822 22.1376 630.2958 PLG4 2171.6101 2018.7130 -1994.7130 22.0755 636.3475 PLG4 2200.8000 2047.4358 -2023.4358 22.4943 641.5269 PLG4 2229.9800 2076.3047 -2052.3047 23.0057 645.7305 PLG4 2259.1399 2105.2910 -2081.2910 23.2804 648.8818 PLG4 2288.4500 2134.5322 -2110.5322 23.0263 650.8096 PLG4 2317.8000 2163.8618 -2139.8618 22.2171 651.4882 PLG4 2347.2100 2193.2510 -2169.2510 21.1321 651.6870 PLG4 2376.5100 2222.5300 -2198.5300 20.0335 651.8088 PLG4 2405.6699 2251.6711 -2227.6711 18.9916 651.8930 PLG4 2434.7800 2280.7627 -2256.7627 17.9532 651.9476 PLG4 2463.8501 2309.8123 -2285.8123 16.8614 651.9869 PLG4 2493.0200 2338.9583 -2314.9583 15.6852 652.0544 PLG4 2522.1201 2368.0330 -2344.0330 14.4733 652.1174 PLG4 2544.6001 2390.4927 -2366.4927 13.5210 652.1406 PLG4 2572.3999 2418.2783 -2394.2783 12.6551 652.0626 PLG4 2603.9399 2449.8120 -2425.8120 12.0580 651.8641 PLG4 2630.9800 2476.8452 -2452.8452 11.4990 651.6353 PLG4 2658.0000 2503.8547 -2479.8547 10.8241 651.3008 PLG4 2687.2600 2533.0996 -2509.0996 10.0013 650.8527 PLG4 2716.6201 2562.4419 -2538.4419 9.1155 650.3487 PLG4 2745.7100 2591.5149 -2567.5149 8.2724 649.8350 PLG4 2774.8101 2620.5999 -2596.5999 7.4545 649.3796 PLG4 2803.8899 2649.6643 -2625.6643 6.6437 648.9006 PLG4 2833.9900 2679.7493 -2655.7493 5.8501 648.3774 PLG4 2865.5300 2711.2764 -2687.2764 5.1379 647.8224 PLG4 2966.3799 2812.0925 -2788.0925 3.2836 645.9931 PLG4 3013.9600 2859.6594 -2835.6594 2.6154 645.0961 PLG4 3041.7000 2887.3916 -2863.3916 2.4731 644.4565 PLG4 3070.9399 2916.6182 -2892.6182 2.6828 643.6044 PLG4 3100.1399 2945.7944 -2921.7944 3.2629 642.5878 PLG4 3130.2600 2975.8767 -2951.8767 4.1646 641.3809 PLG4 3152.3999 2997.9788 -2973.9788 5.0025 640.3981 PLG4 3170.0000 3015.5452 -2991.5452 5.7240 639.5854

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Well Data Export Project : NORNE Cartographic System : ST_ED50_UTM32N_CM9NE Depth Mode : MD Depth Unit : International meters Distance Unit : meters Date : Thu Dec 27 14:55:22 2007 PLG1 UWI: NO 6608/10-F-3 H PLG2 Common: 6608/10-F-3 H PLG3 Measr. Depth TVD Depth TVDSS Depth X-Offset Y-Offset PLG4 0.0000 0.0000 22.0000 0.0000 0.0000 PLG4 402.0000 402.0000 -380.0000 0.0000 0.0000 PLG4 403.0000 403.0000 -381.0000 -0.0002 0.0000 PLG4 517.2000 517.1611 -495.1611 -2.5481 0.4686 PLG4 545.4000 545.3137 -523.3137 -4.1633 0.5974 PLG4 573.4000 573.2251 -551.2251 -6.3826 0.6771 PLG4 602.1000 601.7985 -579.7985 -9.0678 0.8510 PLG4 630.7000 630.1990 -608.1990 -12.4161 0.7028 PLG4 659.7000 658.8725 -636.8725 -16.6831 -0.0502 PLG4 688.8000 687.4946 -665.4946 -21.7155 -1.5098 PLG4 717.3200 715.3413 -693.3413 -27.2663 -4.1426 PLG4 745.8600 742.9384 -720.9384 -33.2828 -8.2110 PLG4 773.8800 769.7471 -747.7471 -39.7640 -13.1442 PLG4 802.3300 796.7752 -774.7752 -46.8473 -18.5015 PLG4 859.4500 850.3327 -828.3327 -62.4841 -30.6965 PLG4 887.9000 876.4516 -854.4516 -71.2981 -37.7243 PLG4 916.6000 902.2076 -880.2076 -81.1276 -45.6918 PLG4 944.8000 926.8836 -904.8836 -91.4915 -54.5682 PLG4 973.1000 951.0951 -929.0951 -102.7786 -63.9033 PLG4 1001.8000 975.1949 -953.1949 -115.2404 -73.2589 PLG4 1030.3000 998.7864 -976.7864 -128.0585 -82.8169 PLG4 1059.0000 1022.1158 -1000.1158 -141.4573 -92.8072 PLG4 1087.8000 1044.9757 -1022.9757 -155.6324 -103.0963 PLG4 1116.4200 1067.1611 -1045.1611 -170.0754 -113.9692 PLG4 1144.8300 1088.5389 -1066.5389 -184.8372 -125.4622 PLG4 1173.5000 1109.2396 -1087.2396 -200.9514 -137.0139 PLG4 1202.2000 1129.2371 -1107.2371 -218.1263 -148.3619 PLG4 1230.4000 1148.3678 -1126.3678 -235.4078 -159.7879 PLG4 1258.7000 1167.2711 -1145.2711 -252.9354 -171.4641 PLG4 1287.1000 1186.3002 -1164.3002 -270.4308 -183.2269 PLG4 1315.2000 1205.3081 -1183.3081 -287.4364 -195.0204 PLG4 1343.7000 1224.7744 -1202.7744 -304.5216 -206.9111 PLG4 1372.3000 1244.1644 -1222.1644 -321.7630 -218.9394 PLG4 1401.0000 1263.4595 -1241.4595 -339.0543 -231.2845 PLG4 1461.0000 1304.9578 -1282.9578 -374.2129 -256.6035 PLG4 1491.1000 1325.5103 -1303.5103 -392.0629 -269.4323 PLG4 1519.6000 1343.4871 -1321.4871 -410.1084 -282.2054 PLG4 1547.9000 1360.6561 -1338.6561 -428.4916 -295.1733 PLG4 1576.6000 1378.0229 -1356.0229 -446.7768 -308.8698 PLG4 1605.3000 1395.3831 -1373.3831 -464.7795 -322.9484 PLG4 1633.7000 1412.7804 -1390.7804 -482.3618 -336.9021 PLG4 1662.2000 1430.4406 -1408.4406 -499.8342 -350.8684 PLG4 1690.8000 1448.4487 -1426.4487 -517.2722 -364.6365 PLG4 1719.5000 1466.8044 -1444.8044 -534.4603 -378.4682 PLG4 1748.1000 1485.3159 -1463.3159 -551.4583 -392.1187 PLG4 1776.6000 1503.6272 -1481.6272 -568.5441 -405.7193 PLG4 1805.1000 1521.7439 -1499.7439 -585.7070 -419.4839 PLG4 1833.7000 1540.0817 -1518.0817 -602.7404 -433.3238 PLG4 1862.1000 1558.5183 -1536.5183 -619.4650 -446.9958

PLG4 1890.9000 1576.8827 -1554.8827 -636.7690 -460.8743 PLG4 1920.1000 1595.1674 -1573.1674 -654.6476 -474.9686 PLG4 1949.1000 1613.6819 -1591.6819 -672.0578 -488.9351 PLG4 1977.6000 1632.2213 -1610.2213 -688.9131 -502.5150 PLG4 2006.3000 1651.2515 -1629.2515 -705.6994 -515.9219 PLG4 2035.0000 1670.0739 -1648.0739 -722.7280 -529.3129 PLG4 2063.8000 1688.6975 -1666.6975 -740.0546 -542.8175 PLG4 2092.5000 1707.5361 -1685.5361 -757.2111 -556.0242 PLG4 2121.1001 1726.3824 -1704.3824 -774.2991 -569.0920 PLG4 2149.2000 1744.4572 -1722.4572 -791.3945 -582.1518 PLG4 2177.8000 1762.5312 -1740.5312 -809.1369 -595.4370 PLG4 2206.2000 1780.6263 -1758.6263 -826.7015 -608.4984 PLG4 2234.8000 1798.9891 -1776.9891 -844.3000 -621.5776 PLG4 2263.3999 1817.5309 -1795.5309 -861.7711 -634.5742 PLG4 2292.0000 1836.2339 -1814.2339 -879.1608 -647.4490 PLG4 2320.8000 1855.1210 -1833.1210 -896.7169 -660.2747 PLG4 2349.2000 1873.4666 -1851.4666 -914.3396 -672.8995 PLG4 2377.8999 1891.8396 -1869.8396 -932.3645 -685.5964 PLG4 2406.2000 1910.2059 -1888.2059 -950.0983 -697.8058 PLG4 2434.8000 1928.9502 -1906.9502 -967.6987 -710.3264 PLG4 2463.7000 1947.6128 -1925.6128 -985.4360 -723.4501 PLG4 2492.3000 1965.8247 -1943.8247 -1003.3900 -736.2529 PLG4 2521.0000 1984.1887 -1962.1887 -1021.3610 -749.0382 PLG4 2549.6001 2001.9716 -1979.9716 -1039.4771 -762.2059 PLG4 2578.3000 2019.4436 -1997.4436 -1057.7457 -775.7934 PLG4 2606.7000 2037.1034 -2015.1034 -1075.4678 -789.2316 PLG4 2635.3999 2055.2446 -2033.2446 -1093.3433 -802.4618 PLG4 2663.8000 2073.4663 -2051.4663 -1110.7513 -815.5556 PLG4 2692.5000 2092.2231 -2070.2231 -1128.0127 -828.7422 PLG4 2720.8000 2110.9219 -2088.9219 -1144.8496 -841.6938 PLG4 2749.3000 2129.9512 -2107.9512 -1161.5270 -854.8082 PLG4 2777.7000 2148.9028 -2126.9028 -1178.2639 -867.7401 PLG4 2806.1001 2167.0144 -2145.0144 -1195.7473 -880.8780 PLG4 2834.7000 2184.7009 -2162.7009 -1213.6556 -894.4576 PLG4 2863.2000 2202.5811 -2180.5811 -1231.3973 -907.7897 PLG4 2891.8000 2220.7363 -2198.7363 -1249.2164 -920.8597 PLG4 2919.8999 2238.8247 -2216.8247 -1266.4889 -933.6682 PLG4 2948.3000 2257.2883 -2235.2883 -1283.9021 -946.4130 PLG4 2976.8999 2276.0647 -2254.0647 -1301.5089 -958.8781 PLG4 3006.3999 2295.7888 -2273.7888 -1319.1680 -971.8873 PLG4 3034.1001 2314.8013 -2292.8013 -1335.0022 -984.3386 PLG4 3062.3999 2335.1882 -2313.1882 -1350.1567 -996.7994 PLG4 3091.0000 2356.7644 -2334.7644 -1364.2717 -1009.1724 PLG4 3119.7000 2379.2566 -2357.2566 -1377.4344 -1021.1854 PLG4 3147.7000 2402.1353 -2380.1353 -1389.3871 -1032.0248 PLG4 3176.0000 2425.7788 -2403.7788 -1400.7694 -1042.6206 PLG4 3204.6001 2450.2903 -2428.2903 -1411.5814 -1052.6130 PLG4 3233.3999 2475.6570 -2453.6570 -1421.9319 -1061.4893 PLG4 3262.3999 2501.6462 -2479.6462 -1431.7678 -1069.7765 PLG4 3291.5000 2528.0710 -2506.0710 -1441.0775 -1077.6421 PLG4 3319.8000 2554.1470 -2532.1470 -1449.5333 -1084.6580 PLG4 3338.3000 2571.3955 -2549.3955 -1454.7379 -1088.8590 PLG4 3379.5000 2609.8140 -2587.8140 -1466.4454 -1098.0449 PLG4 3408.5000 2636.8940 -2614.8940 -1474.6378 -1104.4121 PLG4 3437.2000 2663.7905 -2641.7905 -1482.5378 -1110.5645

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PLG4 3467.5000 2692.2600 -2670.2600 -1490.6686 -1117.0043 PLG4 3493.3000 2716.5613 -2694.5613 -1497.3850 -1122.4794 PLG4 3524.4299 2746.0066 -2724.0066 -1505.3009 -1128.7533 PLG4 3553.1899 2773.3525 -2751.3525 -1512.3129 -1134.2444 PLG4 3581.8501 2800.7288 -2778.7288 -1518.9012 -1139.5861

PLG4 3610.3799 2828.1375 -2806.1375 -1524.9567 -1144.6846 PLG4 3638.8999 2855.7451 -2833.7451 -1530.3684 -1149.3613 PLG4 3667.5200 2883.6182 -2861.6182 -1535.2211 -1153.6763 PLG4 3696.2900 2911.7637 -2889.7637 -1539.5973 -1157.7212 PLG4 3724.6001 2939.5603 -2917.5603 -1543.4677 -1161.4366

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Well Headers

Name Surface X Surface Y MD E-2H 459199.7681 7325441.3058 4075.0000 E-3AH 459189.8100 7325451.2400 3260.0000 E-3H 459189.8158 7325451.2344 3010.0000 F-1H 459309.3643 7325355.1618 3168.0000 F-3H 459294.7464 7325360.1712 3750.0000

Production and Injection data *METRIC *DAILY *IGNORE_MISSING *HRS_IN_DAYS *YEAR *MONTH *DAY *OIL *WATER *GAS *WINJ *GINJ * DAYS * WIDAY *GIDAY *Name 'E-2H' 1999-11-01 1456.24 0.00 229848.00 0.00 0.00 1.00 1.00 1.00 1999-12-01 4901.33 0.00 1026510.00 0.00 0.00 1.00 1.00 1.00 2000-01-01 5075.85 0.00 1188940.00 0.00 0.00 1.00 1.00 1.00 2000-02-01 5993.18 0.00 1336640.00 0.00 0.00 1.00 1.00 1.00 2000-03-01 4959.45 0.00 1190890.00 0.00 0.00 1.00 1.00 1.00 2000-04-01 3000.22 0.00 692522.00 0.00 0.00 1.00 1.00 1.00 2000-05-01 5072.32 0.00 1387270.00 0.00 0.00 1.00 1.00 1.00 2000-06-01 3873.97 0.00 1162710.00 0.00 0.00 1.00 1.00 1.00 2000-07-01 4963.50 0.00 1045280.00 0.00 0.00 1.00 1.00 1.00 2000-08-01 5009.88 0.00 793598.00 0.00 0.00 1.00 1.00 1.00 2000-09-01 5233.49 0.00 854764.00 0.00 0.00 1.00 1.00 1.00 2000-10-01 5260.90 0.00 878973.00 0.00 0.00 1.00 1.00 1.00 2000-11-01 5209.23 0.00 928630.00 0.00 0.00 1.00 1.00 1.00 2000-12-01 4552.95 0.00 832146.00 0.00 0.00 1.00 1.00 1.00 2001-01-01 4334.01 0.00 684360.00 0.00 0.00 1.00 1.00 1.00 2001-02-01 5792.53 0.00 949277.00 0.00 0.00 1.00 1.00 1.00 2001-03-01 6372.33 0.00 1007140.00 0.00 0.00 1.00 1.00 1.00 2001-04-01 7989.05 0.00 1130220.00 0.00 0.00 1.00 1.00 1.00 2001-05-01 7721.60 0.00 1103550.00 0.00 0.00 1.00 1.00 1.00 2001-06-01 7038.67 0.00 797902.00 0.00 0.00 1.00 1.00 1.00 2001-07-01 6371.44 0.00 575584.00 0.00 0.00 1.00 1.00 1.00 2001-08-01 6228.67 0.00 501356.00 0.00 0.00 1.00 1.00 1.00 2001-09-01 4627.43 0.00 401146.00 0.00 0.00 1.00 1.00 1.00 2001-10-01 7235.75 0.00 770421.00 0.00 0.00 1.00 1.00 1.00 2001-11-01 7245.75 0.00 778546.00 0.00 0.00 1.00 1.00 1.00 2001-12-01 7243.24 0.00 782511.00 0.00 0.00 1.00 1.00 1.00 2002-01-01 6492.56 14.45 632172.00 0.00 0.00 1.00 1.00 1.00 2002-02-01 6595.31 98.77 731181.00 0.00 0.00 1.00 1.00 1.00 2002-03-01 6302.30 325.15 641180.00 0.00 0.00 1.00 1.00 1.00 2002-04-01 6297.70 659.89 646873.00 0.00 0.00 1.00 1.00 1.00 2002-05-01 5363.15 757.72 586152.00 0.00 0.00 1.00 1.00 1.00 2002-06-01 4110.86 728.11 430383.00 0.00 0.00 1.00 1.00 1.00 2002-07-01 4299.77 966.21 506643.00 0.00 0.00 1.00 1.00 1.00 2002-08-01 4020.65 875.16 472488.00 0.00 0.00 1.00 1.00 1.00 2002-09-01 4757.62 919.16 492004.00 0.00 0.00 1.00 1.00 1.00 2002-10-01 2875.95 687.78 355812.00 0.00 0.00 1.00 1.00 1.00 2002-11-01 3212.81 603.91 432084.00 0.00 0.00 1.00 1.00 1.00

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2002-12-01 3561.37 1029.64 461713.00 0.00 0.00 1.00 1.00 1.00 2003-01-01 3586.26 1048.47 488029.00 0.00 0.00 1.00 1.00 1.00 2003-02-01 3663.65 1151.44 515768.00 0.00 0.00 1.00 1.00 1.00 2003-03-01 3805.97 1316.84 466671.00 0.00 0.00 1.00 1.00 1.00 2003-04-01 2461.00 861.57 267127.00 0.00 0.00 1.00 1.00 1.00 2003-05-01 3226.45 1344.42 366842.00 0.00 0.00 1.00 1.00 1.00 2003-06-01 3419.80 915.23 361843.00 0.00 0.00 1.00 1.00 1.00 2003-07-01 3326.65 1471.00 324237.00 0.00 0.00 1.00 1.00 1.00 2003-08-01 3277.71 1654.10 311002.00 0.00 0.00 1.00 1.00 1.00 2003-09-01 2885.24 1485.82 321646.00 0.00 0.00 1.00 1.00 1.00 2003-10-01 2606.43 1553.57 296659.00 0.00 0.00 1.00 1.00 1.00 2003-11-01 2848.71 2046.59 329507.00 0.00 0.00 1.00 1.00 1.00 2003-12-01 2918.76 2129.47 389602.00 0.00 0.00 1.00 1.00 1.00 2004-01-01 3059.45 1829.63 451537.00 0.00 0.00 1.00 1.00 1.00 *Name 'E-3AH' 2000-12-01 2124.45 0.00 289569.00 0.00 0.00 1.00 1.00 1.00 2001-01-01 2505.03 97.30 472698.00 0.00 0.00 1.00 1.00 1.00 2001-02-01 1609.05 178.78 626600.00 0.00 0.00 1.00 1.00 1.00 2001-03-01 1163.29 129.26 437217.00 0.00 0.00 1.00 1.00 1.00 2001-04-01 34.54 3.84 12390.40 0.00 0.00 1.00 1.00 1.00 2001-05-01 0.00 0.00 0.00 0.00 0.00 1.00 1.00 1.00 2001-06-01 0.00 0.00 0.00 0.00 0.00 1.00 1.00 1.00 2001-07-01 0.00 0.00 0.00 0.00 0.00 1.00 1.00 1.00 2001-08-01 0.00 0.00 0.00 0.00 0.00 1.00 1.00 1.00 2001-09-01 0.00 0.00 0.00 0.00 0.00 1.00 1.00 1.00 2001-10-01 0.00 0.00 0.00 0.00 0.00 1.00 1.00 1.00 2001-11-01 0.00 0.00 0.00 0.00 0.00 1.00 1.00 1.00 2001-12-01 0.00 0.00 0.00 0.00 0.00 1.00 1.00 1.00 2002-01-01 111.85 0.00 10257.00 0.00 0.00 1.00 1.00 1.00 2002-02-01 1330.67 0.00 134602.00 0.00 0.00 1.00 1.00 1.00 2002-03-01 440.49 0.00 44842.10 0.00 0.00 1.00 1.00 1.00 2002-04-01 542.92 0.00 47377.10 0.00 0.00 1.00 1.00 1.00 2002-05-01 360.07 24.90 56695.90 0.00 0.00 1.00 1.00 1.00 2002-06-01 484.87 79.08 106666.00 0.00 0.00 1.00 1.00 1.00 2002-07-01 540.00 95.55 127105.00 0.00 0.00 1.00 1.00 1.00 2002-08-01 398.61 68.16 94053.00 0.00 0.00 1.00 1.00 1.00 2002-09-01 129.65 18.07 27781.00 0.00 0.00 1.00 1.00 1.00 2002-10-01 431.34 61.67 105732.00 0.00 0.00 1.00 1.00 1.00 2002-11-01 302.40 5.98 86834.10 0.00 0.00 1.00 1.00 1.00 2002-12-01 191.23 0.00 58428.30 0.00 0.00 1.00 1.00 1.00 2003-01-01 273.54 0.00 86360.50 0.00 0.00 1.00 1.00 1.00 2003-02-01 369.07 0.00 119355.00 0.00 0.00 1.00 1.00 1.00 2003-03-01 232.00 0.00 65374.70 0.00 0.00 1.00 1.00 1.00 2003-04-01 271.97 2.73 174008.00 0.00 0.00 1.00 1.00 1.00 2003-05-01 231.94 69.74 263155.00 0.00 0.00 1.00 1.00 1.00 2003-06-01 327.17 62.60 355892.00 0.00 0.00 1.00 1.00 1.00 2003-07-01 165.26 43.19 169057.00 0.00 0.00 1.00 1.00 1.00 2003-08-01 268.23 77.61 275137.00 0.00 0.00 1.00 1.00 1.00 2003-09-01 110.54 35.44 131288.00 0.00 0.00 1.00 1.00 1.00 2003-10-01 404.48 138.12 502110.00 0.00 0.00 1.00 1.00 1.00 2003-11-01 334.87 123.85 397588.00 0.00 0.00 1.00 1.00 1.00 2003-12-01 215.98 77.81 265613.00 0.00 0.00 1.00 1.00 1.00 2004-01-01 167.60 58.78 238048.00 0.00 0.00 1.00 1.00 1.00 *Name 'E-3H' 1998-08-01 0.00 0.00 0.00 0.00 0.00 1.00 1.00 1.00 1998-09-01 1300.75 0.00 147419.00 0.00 0.00 1.00 1.00 1.00 1998-10-01 3197.31 0.00 353228.00 0.00 0.00 1.00 1.00 1.00 1998-11-01 4639.44 118.26 553991.00 0.00 0.00 1.00 1.00 1.00 1998-12-01 2700.64 201.72 340426.00 0.00 0.00 1.00 1.00 1.00 1999-01-01 2827.28 359.97 403454.00 0.00 0.00 1.00 1.00 1.00 1999-02-01 2638.25 544.91 288077.00 0.00 0.00 1.00 1.00 1.00 1999-03-01 3090.20 632.93 434241.00 0.00 0.00 1.00 1.00 1.00 1999-04-01 3405.53 734.84 515052.00 0.00 0.00 1.00 1.00 1.00 1999-05-01 2375.76 709.65 295940.00 0.00 0.00 1.00 1.00 1.00 1999-06-01 1826.52 602.97 225660.00 0.00 0.00 1.00 1.00 1.00 1999-07-01 1179.99 380.83 147653.00 0.00 0.00 1.00 1.00 1.00 1999-08-01 2261.81 409.50 275138.00 0.00 0.00 1.00 1.00 1.00

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1999-09-01 2795.77 603.61 372162.00 0.00 0.00 1.00 1.00 1.00 1999-10-01 2807.86 784.32 375266.00 0.00 0.00 1.00 1.00 1.00 1999-11-01 1070.16 348.18 113481.00 0.00 0.00 1.00 1.00 1.00 1999-12-01 1343.59 466.47 174031.00 0.00 0.00 1.00 1.00 1.00 2000-01-01 1877.26 666.11 257822.00 0.00 0.00 1.00 1.00 1.00 2000-02-01 2338.58 864.96 301356.00 0.00 0.00 1.00 1.00 1.00 2000-03-01 2249.77 1034.08 274662.00 0.00 0.00 1.00 1.00 1.00 2000-04-01 2368.30 1220.00 297430.00 0.00 0.00 1.00 1.00 1.00 2000-05-01 0.00 0.00 0.00 0.00 0.00 1.00 1.00 1.00 *Name 'F-3H' 2000-09-01 0.00 0.00 0.00 1007.71 0.00 1.00 1.00 1.00 2000-10-01 0.00 0.00 0.00 5011.47 0.00 1.00 1.00 1.00 2000-11-01 0.00 0.00 0.00 6241.46 0.00 1.00 1.00 1.00 2000-12-01 0.00 0.00 0.00 7183.81 0.00 1.00 1.00 1.00 2001-01-01 0.00 0.00 0.00 7069.27 0.00 1.00 1.00 1.00 2001-02-01 0.00 0.00 0.00 7507.90 0.00 1.00 1.00 1.00 2001-03-01 0.00 0.00 0.00 7894.81 0.00 1.00 1.00 1.00 2001-04-01 0.00 0.00 0.00 7005.19 0.00 1.00 1.00 1.00 2001-05-01 0.00 0.00 0.00 7348.87 0.00 1.00 1.00 1.00 2001-06-01 0.00 0.00 0.00 6839.99 0.00 1.00 1.00 1.00 2001-07-01 0.00 0.00 0.00 7344.38 0.00 1.00 1.00 1.00 2001-08-01 0.00 0.00 0.00 6892.50 0.00 1.00 1.00 1.00 2001-09-01 0.00 0.00 0.00 6083.57 0.00 1.00 1.00 1.00 2001-10-01 0.00 0.00 0.00 6282.13 0.00 1.00 1.00 1.00 2001-11-01 0.00 0.00 0.00 9102.72 0.00 1.00 1.00 1.00 2001-12-01 0.00 0.00 0.00 9845.87 0.00 1.00 1.00 1.00 2002-01-01 0.00 0.00 0.00 8634.16 0.00 1.00 1.00 1.00 2002-02-01 0.00 0.00 0.00 10297.30 0.00 1.00 1.00 1.00 2002-03-01 0.00 0.00 0.00 9687.77 0.00 1.00 1.00 1.00 2002-04-01 0.00 0.00 0.00 10336.00 0.00 1.00 1.00 1.00 2002-05-01 0.00 0.00 0.00 10045.20 0.00 1.00 1.00 1.00 2002-06-01 0.00 0.00 0.00 8920.26 0.00 1.00 1.00 1.00 2002-07-01 0.00 0.00 0.00 8969.73 0.00 1.00 1.00 1.00 2002-08-01 0.00 0.00 0.00 8283.03 0.00 1.00 1.00 1.00 2002-09-01 0.00 0.00 0.00 5979.53 0.00 1.00 1.00 1.00 2002-10-01 0.00 0.00 0.00 3593.70 0.00 1.00 1.00 1.00 2002-11-01 0.00 0.00 0.00 3833.20 0.00 1.00 1.00 1.00 2002-12-01 0.00 0.00 0.00 3830.77 0.00 1.00 1.00 1.00 2003-01-01 0.00 0.00 0.00 3908.40 0.00 1.00 1.00 1.00 2003-02-01 0.00 0.00 0.00 3637.17 0.00 1.00 1.00 1.00 2003-03-01 0.00 0.00 0.00 2766.00 0.00 1.00 1.00 1.00 2003-04-01 0.00 0.00 0.00 3383.47 0.00 1.00 1.00 1.00 2003-05-01 0.00 0.00 0.00 2346.35 0.00 1.00 1.00 1.00 2003-06-01 0.00 0.00 0.00 3967.90 0.00 1.00 1.00 1.00 2003-07-01 0.00 0.00 0.00 4199.42 0.00 1.00 1.00 1.00 2003-08-01 0.00 0.00 0.00 4091.74 0.00 1.00 1.00 1.00 2003-09-01 0.00 0.00 0.00 5213.44 0.00 1.00 1.00 1.00 2003-10-01 0.00 0.00 0.00 4220.07 0.00 1.00 1.00 1.00 2003-11-01 0.00 0.00 0.00 4194.05 0.00 1.00 1.00 1.00 2003-12-01 0.00 0.00 0.00 4584.42 0.00 1.00 1.00 1.00 2004-01-01 0.00 0.00 0.00 4291.68 0.00 1.00 1.00 1.00 *Name 'F-1H' 1999-05-01 0.00 0.00 0.00 0.00 0.00 1.00 1.00 1.00 1999-06-01 0.00 0.00 0.00 0.00 0.00 1.00 1.00 1.00 1999-07-01 0.00 0.00 0.00 0.00 0.00 1.00 1.00 1.00 1999-08-01 0.00 0.00 0.00 0.00 0.00 1.00 1.00 1.00 1999-09-01 0.00 0.00 0.00 7792.45 0.00 1.00 1.00 1.00 1999-10-01 0.00 0.00 0.00 9162.64 0.00 1.00 1.00 1.00 1999-11-01 0.00 0.00 0.00 5540.28 0.00 1.00 1.00 1.00 1999-12-01 0.00 0.00 0.00 9262.04 0.00 1.00 1.00 1.00 2000-01-01 0.00 0.00 0.00 5840.72 0.00 1.00 1.00 1.00 2000-02-01 0.00 0.00 0.00 6632.60 0.00 1.00 1.00 1.00 2000-03-01 0.00 0.00 0.00 1424.31 0.00 1.00 1.00 1.00 2000-04-01 0.00 0.00 0.00 8842.09 0.00 1.00 1.00 1.00 2000-05-01 0.00 0.00 0.00 7259.35 0.00 1.00 1.00 1.00 2000-06-01 0.00 0.00 0.00 14736.30 0.00 1.00 1.00 1.00

127



2000-07-01 0.00 0.00 0.00 15098.70 0.00 1.00 1.00 1.00 2000-08-01 0.00 0.00 0.00 13682.20 0.00 1.00 1.00 1.00 2000-09-01 0.00 0.00 0.00 14107.70 0.00 1.00 1.00 1.00 2000-10-01 0.00 0.00 0.00 9933.68 0.00 1.00 1.00 1.00 2000-11-01 0.00 0.00 0.00 12468.90 0.00 1.00 1.00 1.00 2000-12-01 0.00 0.00 0.00 11884.00 0.00 1.00 1.00 1.00 2001-01-01 0.00 0.00 0.00 13641.60 0.00 1.00 1.00 1.00 2001-02-01 0.00 0.00 0.00 14649.80 0.00 1.00 1.00 1.00 2001-03-01 0.00 0.00 0.00 15233.40 .00 1.00 1.00 1.00 2001-04-01 0.00 0.00 0.00 14139.20 0.00 1.00 1.00 1.00 2001-05-01 0.00 0.00 0.00 14725.50 0.00 1.00 1.00 1.00 2001-06-01 0.00 0.00 0.00 13707.70 0.00 1.00 1.00 1.00 2001-07-01 0.00 0.00 0.00 14544.90 0.00 1.00 1.00 1.00 2001-08-01 0.00 0.00 0.00 13666.80 0.00 1.00 1.00 1.00 2001-09-01 0.00 0.00 0.00 12168.10 0.00 1.00 1.00 1.00 2001-10-01 0.00 0.00 0.00 11538.00 0.00 1.00 1.00 1.00 2001-11-01 0.00 0.00 0.00 8540.19 0.00 1.00 1.00 1.00 2001-12-01 0.00 0.00 0.00 6111.48 0.00 1.00 1.00 1.00 2002-01-01 0.00 0.00 0.00 8365.82 0.00 1.00 1.00 1.00 2002-02-01 0.00 0.00 0.00 9975.16 0.00 1.00 1.00 1.00 2002-03-01 0.00 0.00 0.00 10360.50 0.00 1.00 1.00 1.00 2002-04-01 0.00 0.00 0.00 9916.17 0.00 1.00 1.00 1.00 2002-05-01 0.00 0.00 0.00 9652.66 0.00 1.00 1.00 1.00 2002-06-01 0.00 0.00 0.00 7563.64 0.00 1.00 1.00 1.00 2002-07-01 0.00 0.00 0.00 8831.79 0.00 1.00 1.00 1.00 2002-08-01 0.00 0.00 0.00 8640.93 0.00 1.00 1.00 1.00 2002-09-01 0.00 0.00 0.00 9610.59 0.00 1.00 1.00 1.00 2002-10-01 0.00 0.00 0.00 9632.87 0.00 1.00 1.00 1.00 2002-11-01 0.00 0.00 0.00 10260.40 0.00 1.00 1.00 1.00 2002-12-01 0.00 0.00 0.00 10289.40 0.00 1.00 1.00 1.00 2003-01-01 0.00 0.00 0.00 10443.80 0.00 1.00 1.00 1.00 2003-02-01 0.00 0.00 0.00 9762.62 0.00 1.00 1.00 1.00 2003-03-01 0.00 0.00 0.00 9895.90 0.00 1.00 1.00 1.00 2003-04-01 0.00 0.00 0.00 9672.23 0.00 1.00 1.00 1.00 2003-05-01 0.00 0.00 0.00 7074.00 0.00 1.00 1.00 1.00 2003-06-01 0.00 0.00 0.00 6943.40 0.00 1.00 1.00 1.00 2003-07-01 0.00 0.00 0.00 11292.10 0.00 1.00 1.00 1.00 2003-08-01 0.00 0.00 0.00 10815.80 0.00 1.00 1.00 1.00 2003-09-01 0.00 0.00 0.00 9915.12 0.00 1.00 1.00 1.00 2003-10-01 0.00 0.00 0.00 9757.56 0.00 1.00 1.00 1.00 2003-11-01 0.00 0.00 0.00 7208.50 0.00 1.00 1.00 1.00 2003-12-01 0.00 0.00 0.00 7322.73 0.00 1.00 1.00 1.00 2004-01-01 0.00 0.00 0.00 11094.90 0.00 1.00 1.00 1.00

128



Well Events UNITS METRIC Date Activity Top bottom Diam Skin WELLNAME E-3AH 01.12.2000 Perforation 3949.20 4090.80 0.22 0.00 12.01.2005 Plug 3850.00 WELLNAME F-1H 20.05.1999 Perforation 2858.10 2881.50 0.22 0.00 20.05.1999 Perforation 2887.80 3016.99 0.22 0.00 21.05.1999 Squeeze 2858.10 3016.99 30.08.1999 Perforation 2884.84 2887.39 0.22 0.00 30.08.1999 Perforation 2889.83 2897.97 0.22 0.00 30.08.1999 Perforation 2898.07 2908.04 0.22 0.00 30.08.1999 Perforation 2908.05 2912.96 0.22 0.00 30.08.1999 Perforation 2912.96 2913.92 0.22 0.00 30.08.1999 Perforation 2915.42 2917.00 0.22 0.00 30.08.1999 Perforation 2918.70 2945.70 0.22 0.00 WELLNAME E-2H 13.11.1999 Perforation 3221.80 3368.20 0.22 0.00 13.11.1999 Perforation 3706.40 3831.26 0.22 0.00 13.11.1999 Perforation 3585.92 3654.80 0.22 0.00 13.11.1999 Perforation 3419.70 3523.02 0.22 0.00 13.11.1999 Perforation 3865.70 4025.10 0.22 0.00 10.07.2005 Plug 3220

WELLNAME E-3H 30.08.1998 Perforation 2706.30 2742.00 0.22 0.00 30.08.1998 Perforation 2749.50 2911.00 0.22 0.00 31.08.1998 Squeeze 2706.30 2911.50 18.09.1998 Perforation 2785.00 2788.00 0.22 0.00 18.09.1998 Perforation 2774.49 2776.21 0.22 0.00 18.09.1998 Perforation 2778.72 2781.00 0.22 0.00 18.09.1998 Perforation 2772.00 2775.00 0.22 0.00 18.09.1998 Perforation 2758.48 2762.00 0.22 0.00 18.09.1998 Perforation 2762.00 2762.70 0.22 0.00 18.09.1998 Perforation 2765.26 2770.00 0.22 0.00 18.09.1998 Perforation 2746.16 2749.54 0.22 0.00 18.09.1998 Perforation 2755.59 2759.00 0.22 0.00 05.08.1999 Plug 2775.00 WELLNAME F-3H 9.09.2000 Perforation 3348.10 3389.10 0.22 0.00 9.09.2000 Perforation 3395.30 3608.02 0.22 0.00 10.09.2000 Squeeze 3348.72 3608.02 10.09.2000 Squeeze 3395.73 3608.02 21.09.2000 Perforation 3513.79 3533.79 0.22 0.00 21.09.2000 Perforation 3535.08 3536.08 0.22 0.00 22.09.2000 Perforation 3450.00 3479.63 0.22 0.00 22.09.2000 Perforation 3482.95 3493.32 0.22 0.00 15.09.2002 Squeeze 3450.00 3479.63 15.09.2002 Squeeze 3482.95 3493.32

“history matching and uncertainty assessment of the norne field e

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