development of surrogate reservoir models (srm) for...

1

Development of Surrogate Reservoir Models (SRM) For Fast Track Analysis of Complex Reservoirs

Shahab D. Mohaghegh, WVU & ISIModavi, A., Hafez, H. Haajizadeh, M., Kenawy, M., and Guruswamy, S.,

Abu Dhabi Company for Onshore Oil Operations - ADCO

SPE 99667

SPE Intelligent Energy Conference, Amsterdam, The Netherlands, April 2006

2

SPE 99667

Shahab D. Mohaghegh, Ph.D. (WVU & ISI)

The Bottle-Neck

Real-Time, High Frequency Data Stream

Full Field Models for Reservoir Simulation & Modeling. One of

the major tools for integrated Reservoir Management

Time Scale:

Seconds, Minutes, Hours

Time Scale:

Days, Months, ….

3

SPE 99667


ObjectiveDeveloping the next generation of intelligent applications as enabling technologies in response to the needs of smart fields.Development of a Surrogate Reservoir Model (SRM) based on a Full Field Model (FFM) for a giant oil field in the Middle East.

4

SPE 99667


FFM CharacteristicsFull Field Model Characteristics:

Underlying Complex Geological Model.ECLIPSETM

165 Horizontal Wells.Approximately 1,000,000 grid blocks.Single Run = 10 Hours on 12 CPUs.Water Injection for Pressure Maintenance.

5

SPE 99667


SRM CharacteristicsAccurate replication of Full Field Model Results (for every well in the field):

Instantaneous Water CutCumulative Oil ProductionCumulative Water Production

Ability to run in real-time.Remove the bottleneck.

6

SPE 99667


Surrogate Reservoir ModelsA subset of a more general set of models called Surrogate Intelligent Models

Real-Time OptimizationReal-Time Decision MakingAnalysis of Uncertainty

An absolute essential tool for smart fields (i-fields)

7

SPE 99667


SRM an Engineering ToolAre Surrogate Reservoir Models the same as “Response Surface” techniques?NO.Unlike purely statistical techniques, SRMs are designed to be engineering tools.

8

SPE 99667


SRM an Engineering ToolDepending on the project objectives, SRMs are developed to preserve and respond to the physics of the problem.Honoring the physics is an important validation step in the development process of SRMs.

9

SPE 99667


Surrogate Reservoir ModelsProject Objectives:

To create an accurate surrogate model that can mimic the Full Field Model.Use the surrogate model to perform Monte Carlo Simulation to quantify the uncertainties associated with the FFM.

10

SPE 99667


Surrogate Reservoir ModelsProject Objectives:

It can provide a foundation for:Analysis of Uncertainty (Monte Carlo Simulation)Real Time OptimizationReal Time Decision MakingAutomatic History Matching

11

SPE 99667


Surrogate Reservoir Models

Methodology:Identify the specific objective of the Surrogate Model. Identify the necessary information needed to accomplish the objective (understand the physics).Resolve the major issue related to the “curse of dimensionality”Develop and validate the Surrogate Model

12

SPE 99667


Methodology

Identify the specific objective of the Surrogate Model.

Analysis of uncertainty.Develop a bean-up schedule for the wells in the asset.Optimize the oil production from the asset (minimize left behind oil).

13

SPE 99667


Very Complex Geology

14

SPE 99667



15

SPE 99667



Reservoirs represented in the FFM.

16

SPE 99667


Curse of DimensionalitySource of dimensionality:

STATIC: Representation of reservoir properties associated with each well.DYNAMIC: Simulation runs to demonstrate well productivity.

17

SPE 99667


Curse of Dimensionality, Static

Representing reservoir properties for horizontal wells.

18

SPE 99667


Curse of Dimensionality, StaticPotential list of parameters that can be collected on a “per-well” basis.

IMPORTANT NOTE: Specific objective of the surrogate model must be identified in advance.

Latitude Longitude

Deviation Azimuth

Horizontal Well Length Productivity Index

Distance to Free Water Level Water Cut @ Reference Point

Flowing BHP @ Reference Point Oil Prod. Rate @ Reference Point

Cum. Oil Prod. @ Reference Point Cum. Water Prod. @ Reference Point

Distance to Nearest Producer Distance to Nearest Injector

Distance to Major Fault Distance to Minor Fault

Parameters Used on a per well basis

19

SPE 99667


Curse of Dimensionality, Static

Potential list of parameters that can be collected on a “per-grid block” basis.

IMPORTANT NOTE: Specific objective of the surrogate model must be identified in advance.

Mid Depth Thickness

Relative Rock Ttype Porosity

Initial Water Saturations Stylolite Intensity

Horizontal Permeabil ity Vertical Permeabil ity

Sw @ Reference Point So @ Reference Point

Capil lary Pressure/Saturation Function Pressure @ Reference Point

Parameters Used on a per segment basis

20

SPE 99667


Curse of Dimensionality, StaticTotal number of parameters that need representation during the modeling process:

18 parameters x 40 grid block/well = 720

12 parameter per well

Total of 732 parameter per well

Building a model with 732 parameters per well is not realistic, THE CURSE OF DIMENSIONALITY

Dimensionality Reduction becomes a vital task.

21

SPE 99667


Curse of Dimensionality, Dynamic

Well productivity is identified through following simulation runs:

All wells producing at 1500, 2500, 3500, & 4500 bpd (nominal rates)

No cap on field productivityCap the field productivity

22

SPE 99667


Curse of Dimensionality, Dynamic

Well productivity through following simulation runs:

Step up the rates for all wellsNo cap on field productivityCap the field productivity

23

SPE 99667


Curse of Dimensionality

In order to address the “Curse of Dimensionality” one must understand the behavior and contribution of each of the parameters to the process being modeled.Not a simple and straight forward task. !!!

24

SPE 99667


Curse of DimensionalityTo address this issue, we use ISI’s Fuzzy Pattern Recognition technology.

25

SPE 99667


Parameter: Pressure @ Reference

Key Performance Indicator

26

SPE 99667


Key Performance Indicator

27

SPE 99667


Key Performance IndicatorsPotential Benefits:

This analysis would confirm or dispute hypotheses and findings, purely based on the collected data:

GeologyPetrophysicsDynamic Data

28

SPE 99667


Key Performance Indicators

29

SPE 99667


Please Note: The lower the bar, the higher the influence.

Key Performance Indicators

30

SPE 99667


Candidate Selection

31

SPE 99667


Surrogate ModelingMethodology:

Divided the data into three partitions.Training dataset – 40%Calibration dataset– 20 %Verification dataset – 40%

Train, and validate the model.Run the model for the ranked candidate wells to decide on the most efficient production strategy.

32

SPE 99667


Surrogate Modeling

33

SPE 99667


Surrogate Modeling

34

SPE 99667


Optimal Production Strategy

Well Ranked No. 1

IMPORTANT NOTE: This is NOT a Response Surface – SRM was run hundreds of times to generate these figures.

35

SPE 99667


Optimal Production Strategy

Well Ranked No. 100

IMPORTANT NOTE: This is NOT a Response Surface – SRM was run hundreds of times to generate these figures.

36

SPE 99667


Analysis of Uncertainty

Objective:To address and analyze the uncertainties associated with the Full Field Model using Monte Carlo simulation method.

37

SPE 99667



Motivation:The Full Field Model is a reservoir simulator that is based on a geologic model. The geologic model is developed based on a set of measurements (logs, core analysis, seismic, …) and corresponding geological and geophysical interpretations.

38

SPE 99667



Motivation:Therefore, like any other reservoir simulation and modeling effort, it includes certain obvious uncertainties.One of the outcomes of this project has been the identification of a small set of reservoir parameters that essentially control the production behavior in the horizontal wells in this field (KPIs).

39

SPE 99667


Following are the steps involved:1. Identify a set of key performance indicators

that are most vulnerable to uncertainty.2. Define probability distribution function for

each of the performance indicators.a. Uniform distributionb. Normal (Gaussian) distributionc. Triangular distributiond. Discrete distribution


40

SPE 99667


Following are steps involved:3. Run the neural network model hundreds or

thousands of times using the defined probability distribution functions for the identified reservoir parameters. Performing this analysis using the actual Full Field Model is impractical.

4. Produce a probability distribution function for cumulative oil production and the water cut at different time and liquid rate cap.


41

SPE 99667


Following are steps involved:5. Such results bounds to be much more

reliable and therefore, more acceptable to the management or skeptics of the reservoir modeling studies.


42

SPE 99667



43

SPE 99667



44

SPE 99667


Average Sw @ Reference point in Top Layer II

Value in the model = 8%Lets use a minimum of 4% and a maximum of 15% with a triangular distribution

4 8 15


45

SPE 99667


Average Capillary Pressure @ Reference point in Top Layer III

Value in the model = 79 psiLets use a minimum of 60 psi and a maximum of 100 psi with a triangular distribution

60 80 100


46

SPE 99667



PDF for HB001 Cumulative Oil and Cumulative Water production at the rate of 3,000 blpd cap after 20 years.

47

SPE 99667


Such analysis can be performed for all wells at any rate and any number of years.There is a higher probability of acceptance of the ideas for rate increase by the management, if we show that:

We are aware of the uncertainties associated with our analysis.Uncertainties are being accounted for in our decision making process.


48

SPE 99667


CONCLUSIONSA successful surrogate reservoir model was developed for a giant oil field in the Middle East.The surrogate model was able to accurately mimic the behavior of the actual full field flow model.

49

SPE 99667


CONCLUSIONSThe surrogate reservoir model would provide results in real time.The surrogate model was used successfully to analyze uncertainties associated with the full field flow model.This approach and methodology is an important and essential step toward

50

SPE 99667


CONCLUSIONSDevelopment of successful surrogate reservoir model is an important and essential step toward development of next generation of reservoir management tools that would address the needs of smart fields.

development of surrogate reservoir models (srm) for...

Documents