Abstract— In this paper, a sequence stratigraphy and reservoir petrophysical analysis of a single oil

well (Well H) has been prepared by using an available core data and wireline logging data with a

view to characterizing the reservoir. In addition, petrophysical analysis initiated with lithology

identification and lithological panels interpreted from well log data show that the study area is

characterized by sand-shale interbedding. Moreover, appropriate logs have been used to interpret

the reservoir for their fluid content, as are result; hydrocarbons versus water bearing zones were

outlined. Furthermore, the reservoir interval generally presents vertical anisotropy and great

heterogeneity with thick sand filled channel layers. The core involves mostly of red bed of sandstone

with some structure-less which is deposited in fluvial environment system. However, the core data

are relatively collated to wireline data to assess and survey the reservoir rock petrophysical

properties. A hydrocarbon water contact (HWC) was obtained from 1655 m. In addition, an average

Geological and Petrophysical Evaluation for an Oil WellHaval Hawez

Petroleum Engineering Department, Koya University

Email: haval.hawez@koyauniversity.org

Mobile: +964(0)7504353785

Daniel Mitterrand Boulevard, Koya KOY45 AB64, Kurdistan Region - Iraq

Zheno Ahmed

Petroleum Engineering Department, Koya University

Email: zheno.ahmed@koyauniversity.org

Mobile: +964(0)7510688746

Daniel Mitterrand Boulevard, Koya KOY45 AB64, Kurdistan Region - Iraq

Warzer M. Salih

Petroleum Engineering Department, Koya University

Email: warzer.jalal@koyauniversity.org

Mobile: +964(0)7512149335

Daniel Mitterrand Boulevard, Koya KOY45 AB64, Kurdistan Region - Iraq

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of 78.5 m of gross rock was decomposed with a counted 17.35 m of net pay exhibit average values of

0.2832 for water saturation and 0.1785 for porosity.

(Key Words: Formation Evaluation, Wireline logging, Petrophysical properties, Core data, Gross

Rock).

I. INTRODUCTION

It is believed that most reservoir hydrocarbons have been existed in microscopic pore spaces or high

conductive fractures of sedimentary rocks, such as: sandstones. Therefore, detailed information about

geological, petrophysical properties of the reservoir are required to optimize of hydrocarbon recovery and

improvement of reservoir performance and to guide the placement of production platforms and well paths

(Imasuen and Samuel, 2013).

According to (Schlumberger, 1989) the studying of the spatial uniformity of the saturating reservoir

fluids can be crucial to oil and gas production. Several researches which carried done by (Adeoye, and

Enikauselu, 2009), (Aigbedion, and Iyayi, 2007) showed that the estimation of lithology, fluid content,

porosity as well as shaliness (a measure of cleanliness of the reservoirs) has a significant consideration in

the evaluation of clastic reservoirs.

In the present study, an oil field has been quantitatively evaluated by interpreting wireline logs and core

data from single well. The main objective of the work is to determine and identify the hydrocarbon bearing

sand bodies (gross rock) as well as to estimate the amount/type of hydrocarbons in the reservoir for

calculation of the reserves. This study is also to demonstrate the understanding of the petrophysical

application of wireline logs in hydrocarbon evaluation and its significance in oil exploration and

exploitation in regions similar to the study area.

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II. LITERATURE REVIEW

Formation Evaluation (FE) has been identified by (Bowman and etc., 1993) and (Stat Oil Research

Group, 2003): as the process of interpreting a combination of measurements taken inside a wellbore to

detect and quantify oil and gas reserves in the rock adjacent to the well. FE data can be gathered with

wireline logging instruments or logging-while-drilling tools. Study of the physical properties of rocks and

the fluids contained within them.

Archie (1950) have specified that the presence of clay particles or shale within the sand is a parameter

which must be considered in the evaluation of a clastic reservoir. Since, both formation characteristic and

logging tool response can be affected by the existed shaliness in the sand formation. In the other hand,

limestone and dolomite are the characterization of carbonates, non-clastic reservoirs and their importance

should not be under estimated as reservoirs rocks (Schlumberger, 1985). In addition, the chemical nature of

matrix and pore fluids primarily impact on the response of well logging tools. Any porous network is

related to its host rock fabric, therefore petrophysical parameter, such as porosity (φ), permeability (K) and

saturation (S), for any given (type of rock) are controlled by pore sizes and their distribution and

interconnection. In order to predict the spatial distribution of such petrophysical parameter on a field scale,

the reservoir characterization must be studied (Archie, 1950). According to (Lucia, F.G., 1983) in the

interpretation of reservoir geophysics observation, petrophysics’ theory and rock physics data should be

analyzed carefully and purposefully.

Petrophysical evaluation has been identified by (Newell, 2006) as the continuing process of integrating

and interpreting geological, petrophysical, fluid and performance data of a reservoir sand body to form a

unified, consistent description of reservoir properties throughout the field. Furthermore, the quality,

quantity, recoverability of hydrocarbon in a reservoir can be determined by applying petrophysical

evaluation within the rock proportion of the reservoir. Therefore, the potential and performance of a

reservoir include porosity, permeability and fluid saturation which are fundamental parameters of a

reservoir that has the capacity to store fluid and the ability to release and flow in it. A reservoir can be

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evaluated and identified by knowing the relationships among these properties. Moreover, shaliness which is

a measure of the cleanliness of the reservoir is a parameter to be considered in the evaluation of clastic

reservoirs as it can give a wrong impression of estimated petrophysical values, such as: porosity and

hydrocarbon saturation when they are not corrected for (Aiyedogbon and Iyayi, 2007).

This research work has been done for a single oil well based on the use of wireline logs and core data from

the well to identify and quantify hydrocarbon reserves and evaluate rock properties in the subsurface. The

petrophysical analysis with wireline logs provides reservoir qualities (porosity, permeability, and fluid

saturation), which were integrated with other data provided a guide and enhanced exploration and

development of the reservoir sand bodies. This can consequently help to optimize hydrocarbon recovery,

and to improve predictions of well and reservoir performance (Adeoye, and Ofomola, 2013). Each

sequence can be sub-divided into smaller sediment packages called systems tracts on the basis of

characteristic well-log patterns (Ola-Buraimo et al, 2010). Sequence analysis and system tract study can

allow us to predict the environment of deposition and this can be related to the petrophysics value obtained.

III. AIM OF THE STUDY

The aim of this paper is to interpret the depositional environment and making a comparison between core

data and petrophysical data which used to verify log interpretation. The main techniques used to represent

and describe the petrophysical and geological studies required to construct a static model of the subsurface

of the reservoir.

The other objectives of this study however is to evaluate the hydrocarbon potential in the well by

petrophysical inference and analysis, and also to identify and describe the depositional environments and

the relationship between physical properties of rocks (from petrophysical analysis) and the depositional

environment of the area.

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IV. METHODOLOGY AND DATA SOURCE

The following data were available while studying this paper:

Available following logging tools: Caliper (Cal), Gamma Ray (GR), Micro Spherical Focused Log

(MSFL), Deep lateral log (LLD/ILD), Deep Induction Log ( ILD), Shallow Lateral Log ( LLS),

Bulk Density Log (RHOB), Compensated Neutron Log (NPHI), Sonic Log (SONI).

Core plugs data (horizontal and vertical permeability and porosity).

Mud log data (mud filtrate resistivity etc.)

Core data (from 1610 to 1680 meters)

Of the total 70 m length of the well was available from 1610 to 1680 meters. A detailed description of

specific 10m interval (from 1644 to 1654 meters) was picked in Table 1. A reservoir geological study

should start by the supervision of recovered core and core samples. The stratigraphic and sedimentalogical

analysis of the subsurface are used to construct a faces model and regenerate the environment of deposition.

A petrophysical analysis is then performed. Core plugs are used to find out the quantity of permeability and

porosity. The core data is then planned in a composite log along with earlier processed wireline log data.

The matching of depth is momentous between core data and wireline data like errors which is caused by

different measuring means might exist. Then data should be studied together after depth shifting quality

control of data and the proceeding of calculation of shale volume, porosity, water resistivity, water

saturation, net pay, reservoir rock and prediction of permeability.

V.RESULTS AND INTERPRETATION

A. Sedimentalogical and environmental description of the reservoir

From the core analysis, we can represent sandstone with block diagram showing fining upward sandstone

with some coarser grains at the bottom which include calcite, carbonate and mud clasts by description of

core sample (Table 1). In addition, it is noticed that the structure-less beds of sandstone are observed with

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thin mudstone layers. As a result of being precipitation of minerals in carbonate rich, carbonate cement

probably is noted through the core during digenesis. The red sandstone is created when iron oxidize with

minerals. As far as logged bodies representing fining upward trends with some cross bedding at the

bottom, it will be assumed that the main depositional environment is fluvial, with some potential flood

plain which is represented by thin layer of mudstone. As a result of mixed Aeolian/fluvial depositional

environment, many structures-less bodies of sandstone were logged. The block diagram in Fig.1 shows a

possible depositional environment for faces body association which included structure-less medium to

coarse sandstone, thin layer of mudstone, and medium to coarse cross bedding sandstone. In addition, some

of the quartz sand might follow from fluvial reworking of Aeolian sandstones.

B. Petrophysical Analysis

1) Core Shifting/ Correlation

It is noted that analysis of core and wireline log data must be checked after depth matching. Sedimentary

logs were not observed strong markers, the gamma ray log were recorded and compared against zones with

Fig. 1. Block diagram with possible depositional environment.

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high mud content where the radiation values were read. According to match theses depths, the shifting core

was 1.35 m and for further analysis plotted with log data. In addition, there are no special outings of GR to

show either clean or sandy shale between intervals 1628 to 1637 and also there are two obvious Gamma

Ray excursions showing dirty sands which are explained to have lower porosity and permeability at about

1627 and 1645 as well as two minimum porosity and permeability are noticed at about 1642 and 1760

(Fig.2).

Depth (m) Description Interpretation Sandbodies Seals/sources

1654-1651.87

1651.87- 1651

1651-1649

1649-1648

1648- 1647

1647- 1644

Erosive surface with fining upward sandstone, interbedded calcite with coarse grain sandstone at the bottom overlain by intraformation mudclasts in the middle with fine grain sand at the tp. ( between 1652.33 to 1652.44, sample removed).

2 Erosive based successions. Erosive surface generally overlain by thick calcite grains followed by interbedded fine sandstone with intra formation mudclasts.

2m interval consisting of 3 erosive based successions. Interbedded mudstone with sand at the bottom. Erosive surfaces generally overlain by thin granule grains (conglomerate) followed by coarse sandstone.(between intervals 1649.25- 1640.37 and 1650- 1650.37, sample removed)

Stacked very fine sandstone at the bottom. Interbedded mudastone with sand followed by interbedded mica with sand at the top. (1648.12- 1648.33, sample removed)

No recovery.

3m interval consisting of stacked coarsening upward sandstone. Fine grained sand at the bottom followed by interbedded intraclasts of mud. (Between intervals 1646.58- 1646.5, 1645.20- 1645.09, and 1644.38- 1644.21 samples removed), (Between 1646to 1845.96 are no recovery).

coarse grained lateral accretion surfaces are observed.

Braided river closely in sequence with thin sheet flood that's fine grained deposits.

In upper parts, sheet floods and coarse grained lateral accretion surfaces are observed.

Arid terrestrial sands.

Laterally discontinuous and vertically heterogeneous arkosic sandbodies are likely to show better but porositiy is variable.Sandbodies areChannelized

Laterally discontinuous baffling shales and drapping sheet floods deposits unlikely to provide long term traps but as barriers will hinder production/recovery higher permeability sands.

TABLE 1. The description of the core (from 1644 to 1654 m).

TABLE 2. Shift table applied for depth matching after shifted down +1.35 m linearly for whole interval.

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2) Volume of shale

In general, volume of shale is used to accurate especially for bound water in porosity and the calculation

of water saturation in later stages.

Fig. 2. Core

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The main log which gives information about the presence of shale is the Gamma Ray log as well as

volume of shale is calculated by Gamma Ray log (eq.1).

IGR = GRLog−GRminGRmax−GRmin (eq. 1)

IGR illustrates shale index volume. GRmax is read from Gamma Ray log which shows the maximum

value of shale and GRmin shows clean interval of sand. Clean sand was selected because the sandstone

includes high value of potassium, Feldspar) which leads to a rise in reading of Gamma Ray log. The

following value GRmin =82 and GRmax =186 were used for calculating the shale volume curve.

3) Porosity

The main log which used for calculation porosity was density tool in the following formula:

∅ = ρma−ρbρma−ρf (eq.2)

Where:

ρma is the matrix density which is given by mudlog data , ρma= 2.71.

ρf is the density of fluid and is given by mudlog data, ρf= 1.00.

ρb is the bulk density from the log.

Because of the considerable presence of shale in the reservoirs, the measured porosity was corrected for

the volume of shale using Dewan (1983):

φcorr=φ−V sh∗φDsh

Where:

φcorr=shale corrected density porosity

φ =Density porosity

Vsh=Shale volume

φDsh=density porosity of nearby shale

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The correlation between core porosity and calculated core porosity is poor as seen in Fig.9. As a result of

structure or digenetic features in the rock, the crossplot scattering points show worse correlation. The

changes between recording porosity by wireline tools and testing porosity in the laboratory by fluid

injection may cause by the amount of mineral dissolution.

4) Water Resistivity

In water bearing zone, Archie's equation uses to calculate the amount of water resistivity. The following

equation use to calculate water resistivity (Rw):

Swn =

F RwRt (eq. 3a)

Where, Sw is the water saturation, n is the saturation exponent.

In water bearing zones assumed water saturation is a 100 percent (Sw=100%), we get:

Rw= RtF (eq. 3b)

Where F is the formation factor and calculated using Humble formula (eq.3c), a: is the constant value

(a=0.62 in sandstone), m: is the cementation factor (m=2.15 in sandstone).

F = a∅m (eq. 3c)

The density log measured porosity which is used in Humble formula using equation 2.

Where: at depth 1723m water resistivity (Rw) and were obtained and then it is calculated by using Gen-6

chart. In this process, where temperature is 136℉ at 1816m at the bottom hole depth (given from the mud-

log) and the mean surface temperature was considered to be around 9℃ (or 48℉). The final temperature

was 135℉ .

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So, Rw is 0.033 ohm at 135℉ and 1723 m deep. After calculating Rw, the amount of salinity of the water

formation can be found out using from Gen-9 chart. The Salinity of the water formation must be about

130,000 ppm at 134℉ and Rw = 0.0343.

Water saturation based on equation 3a and it was calculated using Archie model.

5) Permeability prediction from porosity

The density tool can be used to calculate porosity for correlation with permeability in the absence of core

porosity data.

It may be a difficult exercise in wells where the core data does not exist for permeability prediction. In

addition, some additional data from log and core plugs should be taken from another well to evaluate the

reservoir characteristic behavior better.

6) Lithology

It is a bit trivial in lithology determination for the logged interval. After calculation of shale volume as

previously mentioned, lithology composition is calculated with linear equation for the chosen curves, i.e.,

density, neutron, and delta-t. This is iterated repeatedly until the smallest error. Cross-plot of density

neutron for particular interval was used as double check with the result along with sedimentary log from

core. Figure 8 shows the result is confirmed each other and the calculation can be used for the rest of

interval. Yellow

coloured scattered point in the cross plot is from the whole interval, while the red coloured is from

particular interval of 1630 – 1650 m to have range of 10 m in the core logged.

C. Net-Pay Reservoir Calculation

The knowledge about the net pay is significant for the volumetric hydrocarbon estimation, a practice that

supports the merit of the petroleum industry. There is not general definition of net pay yet, there is not

universal approval of its role in integrated reservoir analysis, there is not identified way for estimating it,

and there are different survey on how to make use of it. Partially for these reasons, net-to-gross pay makes

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up a main source of doubt in volumetric reserves estimates, second merely to gross rock volume. The

process of the recognition of net-pay cutoffs discuss over the years. The access is data-driven, in that it uses

what is known, and also fit-for-purpose, in that it receives statement of reservoir conditions. The result is a

sounder basis for united net pay into volumetric evaluates of extreme recovery and therefore resources of

hydrocarbon.

1) Porosity cutoff

While logarithmic core permeability plotted versus core porosity, the porosity cutoff is chosen for 5.0 mD

of permeability value as shown in Fig.3.

Porosity cutoff ≥ 13

2) Shale cutoff

Porosity is first found by using density tool and is plotted against shale volume curve line. The shale

cutoff is then specified according to previous porosity selected cutoff as seen Fig. 4.

Volume of shale cutoff ≤45

Fig. 3. Kh vs Core plug Phi

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3) Water saturation cutoff

The water saturation cutoff is selected using porosity plot versus water saturation as shown Fig.5.

Furthermore, water saturation is found by water resistivity first and porosity is specified by density tool.

Water saturation cutoff ≤ 73

Fig. 4.

Fig. 5. SwE vs

Effective Phi

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VI. DISCUSSION

A. Petrophysical Summary Report

The sharp decrease of reading resistivity tool shows the hydrocarbon water contact while comparing the

signal of Neutron and Density tools at about 1655 m depth. Although, with decrease of both Neutron and

Density tool can detect gas zones, there is not gas present.

Totally, logged interval was around 170 m and the gross interval was estimated 120 meters and net to

gross ratio was 0.5 m. The resistivity curves was run to provide total resistivity (Rt) and flushed zone

resistivity (Rxo) and then to evaluate water resistivity (Rw). After borehole correction using down-hole

electrical logs, washout was noticed of permeable zone around 1730m and 1755m as well as

unconsolidated clays observed around 1768m. Porosity was found by density tool and permeability was

predicted by various permeability predictors. The followed table shows the petorphyscal summary which

has been done (Table 3):

B. Statistical Analysis:

In future study, heterogeneity, the variation of permeability and anisotropy are analyzed with the assist of

statistical analysis. Therefore, the well data is demanded for spatial information to help the growth reservoir

model and further simulation.

1) Heterogeneity, Average Permeability and Anisotropy

The convenient average should be chosen for analyzing horizontal permeability (Arithmetic, Geometric

or Harmonic average). In fact, this based on the distribution of geological layers and bed geometry. There

are available core data between 1610 to1680 meters and the statistical analysis was created to measure the

degree of heterogeneity in the environment system. The variance of coefficient shows a high degree of

heterogeneity in the permeability data, whereas the variance of coefficient illustrates a low degree of

heterogeneity (Table 3).

As far as the histogram is concerned, porosity illustrates symmetrical distribution that means as a single

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population, whilst permeability histogram shows a skewed distribution and includes two population

evidence data (Fig.6). In addition, the Lorenz plot obviously shows high degree of heterogeneity (Fig.7).

Table 3. Statistical data for Kh and porosity heterogeneity determination.

Fig. 6. Shows histogram for porosity and horizontal permeability.

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Fig. 7. Stratigraphy modified Lorenz plot ordered plug data.

Fig. 8 M-N plot shows lithology

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Fig.9. Semivariogram

C. Conclusions

Analyzing the data of an available core and a suite of well logs has resulted in detailed Petrophysical

analysis and well-log sequence stratigraphy of the well. Adequate lithological interpretation and description

was also carried out with the delineation of hydrocarbon bearing reservoir sands.

In general, the grain size is medium sand porosity which is laid down in an arid environment like a

braided fluvial environmental system. The sand bed is laterally and vertically anisotropic and

heterogeneous, while horizontally flow dominating. The body of faces are baffled by heterogeneous

subsurface.

Although, the net pay is around 17.3502 meters, it may be increase significantly laterally for the layers.

There were no significant signatures for faulting.

Three depositional environments have been interpreted namely: the channel and shoreface environment,

fluvial channels and shoreface sands and the reworked sandstone units. Porosity estimates is highest

observed in the channel and shoreface environment.

To improve the information about the subsurface and reservoir flow unit areas should be more data

collected from other wells in the same area to better understanding of the reservoir characteristic behavior.

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