ADNOC Main Challenges With the Current Simulation Modeling Workflows

AbuDhabi 2013

ADNOC/Schlumberger Simulation Workshop5th December 2013

Faisal Al-Jenaibi

General Simulation Modeling Areas of Concern Static Model:

Stratigraphic and Layering Framework.

Petrophysical Parameters Distribution (permeability, porosity, RRT’s ..etc).

Fluids-in-Place & Water Saturation Modeling.

Vertical/Lateral Transmissibility across Faults.

Upscaling Technology.

Dynamic Model:

Simulation Model, Size and Resolution.

Definition of Transition Zone, SCAL framework issues.

Transition phase between history & prediction modes (VFP’s tables).

High Permeability Streaks and thin Barriers Intervals.

Dual Porosity & Dual Permeability Models.

Upscale/Downscale Sector Model from/to Full Field Model.

Variable “Sor” per RRT based on Wettability.

Streamline Technology.

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General Simulation Modeling Areas of Concern Static Model:

Stratigraphic and Layering Framework.

Petrophysical Parameters Distribution (permeability, porosity, RRT’s ..etc).

Fluids-in-Place & Water Saturation Modeling. (Part-1)

Vertical/Lateral Transmissibility across Faults.

Upscaling Technology.

Dynamic Model:

Simulation Model, Size and Resolution.

Definition of Transition Zone, SCAL framework issues. (Part-2)

Transition phase between history & prediction modes (VFP’s tables).

High Permeability Streaks and thin Barriers Intervals.

Dual Porosity & Dual Permeability Models.

Upscale/Downscale Sector Model from/to Full Field Model.

Variable “Sor” per RRT based on Wettability.

Streamline Technology.

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Part-1: Fluids-in-Place & Water Saturation Modeling Distribute of the FIP’s in the static model should be linked with:

Geological features i.e. (sedimentology, faults, facies, layers pinchout , seismic, ..etc).

Honor and distribute porosity logs profiles.

Classification of RRT’s groups (MICP’s, pore throat distribution, ..etc). Plot “PERM-PORO relationship vs. RRT’s groups”.

Wells Sw_log is the main reference parameter need to be honored and matched, well-by-well to ensure appropriate FIP’s estimation.

End-Point-Scaling approach to be used only with absent of SCAL data.

PORO

PERM

Sw_log

Hei

ght a

bove

FW

L

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Part-1: Water Saturation Model

Simple approach to smoothen cells water saturation nearby FWL

Iteration - 00

co-krigging “stochastic” approach used to distribute Sw_log data in the static model

Iteration - 01

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Part-1: Water Saturation ModelIteration - 00

Iteration - 03

Iteration - 01

Iteration – 06

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Part-1: Water Saturation ModelIteration - 00

Iteration - 03

Iteration - 01

Iteration – 06

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Part-2: The Current Height Function “Pc’s Curves” Design4 Wells, Sw-Logs data

Dep

th,

ft

The height function curves represent thick transition zone. Massive volume of water is mobile at very early time.

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Part-2: Height Function “Pc’s Curves” Design

Facts:

• Many wells which reported with high Sw_log data have produced dry oil during production test although they were completed nearby water zone.

• High porosity & permeability rock type will have lower capillarity force i.e. (Pc curve) than low porosity & permeability rock type.

• Due to high heterogeneity in carbonate reservoir, single Pc curve per rock type might not be enough to reflect Sw_log data.

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SW

Heig

ht

ab

ove

FW

L d

ep

th (

ft)

0 1

Transition Zone

Oil Zone

Oil Dry Limit

FWL Depth

The Current Pc’s Design

Part-2: Height Function “Pc’s Curves” Design

The Current Kr’s Design

SW

Kr’

s C

urv

es

0 1FWL Depth

SwcrSwirr Sor

1

Swcr

In order to slow down water movement in transition zone, either by use:

(1) Unphysical Swcr’s “Simulator Parameter”

(2) Very low Krw’s values

(3) Unsupported permeability multiplier

Swirr

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SW

Heig

ht

ab

ove

FW

L d

ep

th (

ft)

0 1

Water Zone

Transition Zone

Oil Zone

Oil Dry Limit

FWL Depth

The Current Pc’s Design

SWH

eig

ht

ab

ove

FW

L d

ep

th (

ft)

0 1Water Zone

Transition Zone

Oil Zone

High POROHigh PERM

Low POROLow PERM

Oil Dry Limit

FWL Depth

The Proposed Pc’s Design

Part-2: Height Function “Pc’s Curves” Design

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Part-2: The Proposed Height Function “Pc’s Curves” Design4 Wells, Sw-Log data

Dep

th,

ft

PC’s Curves Should:

• Address the thickness of the transition zone.

• Provide excellent match with initial Sw_log data.

• Assist in achieving better history match.

• Contribute in model stability.

• Optimize saturation tables.

• Eliminate Swcr’s usage.

• Address wettability issues.

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Part-2: Dynamic Model Initialization, Case Study-Aug

Static Model “Sw_log” Dynamic Model “Sw_pc”

Co-krigging “stochastic” approach used to distribute

Sw_log data in the static model

Generate 12 drainage Pc’s curves to replicate Sw_log data into dynamic model

Sw_log vs. Sw_pc

Excellent replication of “Sw” static model in the dynamic model has been achieved following applied ADNOC the proposed new Pc’s curves design.

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Part-2: Dynamic Model Initialization, Case Study-Aug

Static Model“Sw_log”

Dynamic Model

“Sw_pc”

Water SaturationCross-Section

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Part-2: Dynamic Model Initialization, Case Study-AugPc’s Curves Examples

Best RRT Intermediate RRT Tight RRT

A total of 194 saturation tables were

used in the Current dynamic model

A total of 24 saturation tables were used in the

updated dynamic model:

12 Drainage Pc’s12 Imbibition Pc’s

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Successful Case Studies

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Successful Case Studies

Part-2: History Match , Case Study-Nov

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Following to implement ADNOC workflow to design Pc’s curves in 2 weeks time frame, massive field GOR and WCT were enhanced.

Conclusions

The current technical challenges and concern issues, which are related to modeling activities, are subject for farther integrated workflows that are requiring very promising technologies and powerful tools in order to address them in batter and practical ways.

The proposed Pc’s curves design showed very encourage results with respect to reproduce static water saturation model into dynamic model at high quality, while contribute in model stability and respect more physics.

Due to the complexity of AbuDhabi reservoirs, with the high uncertainty levels present in most of them, more resolution models are needed to be constructed to reflect reservoirs production behaviors in more accurate mode.

Sharing lessons learned with regard to modeling activities and implemented workflows is essential to maximize knowledge and experiences exchange, while moving into close collaboration to overcome technical challenges.

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Thank You

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