quantifying uncertainties associated with reservoir...
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1Shahab D. Mohaghegh, WVU, ISI
Quantifying Uncertainties Associated with Reservoir Simulation Studies UsingSurrogate Reservoir Models
Shahab D. MohagheghWest Virginia University &Intelligent Solutions, Inc.
SPE 102492
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2Shahab D. Mohaghegh, WVU, ISI SPE 102492
Outline
Reservoir Simulation & UncertaintySurrogate Reservoir ModelsCase Study: A Giant Oil Field In the Middle East.Quantifying Uncertainty, Using Surrogate Reservoir Model
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3Shahab D. Mohaghegh, WVU, ISI SPE 102492
Sources of Uncertainty
Geological InterpretationsLog interpretationsCore measurements & AnalysisSCALRock TypingSeismic measurements and interpretations
THE EARTH MODEL
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4Shahab D. Mohaghegh, WVU, ISI SPE 102492
Quantifying Uncertainty
Conventional Approach:Geo-StatisticsMultiple Realizations
Hundreds & sometimes thousands of simulation runsResponse Surface
New ApproachSurrogate Reservoir Model, SRM
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5Shahab D. Mohaghegh, WVU, ISI SPE 102492
SRM Development
Main tolls for the development of Surrogate Reservoir models: Intelligent Systems
Artificial Neural NetworksGenetic AlgorithmsFuzzy Logic
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6Shahab D. Mohaghegh, WVU, ISI SPE 102492
Surrogate Reservoir Models
A 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)
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7Shahab D. Mohaghegh, WVU, ISI SPE 102492
SRM an Engineering Tool
Are Surrogate Reservoir Models the same as “Response Surface” techniques?
NO. Unlike purely statistical techniques, SRMs are designed to be engineering tools.SRM is defined within the System Theory while Response Surface is a geostatisticalmethod.
INPUT OUTPUTSYSTEM
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8Shahab D. Mohaghegh, WVU, ISI SPE 102492
SRM an Engineering Tool
Depending 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 and their distinguishing feature from other (geostatistical) techniques.
SEE A DEMONSTRATION
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9Shahab D. Mohaghegh, WVU, ISI SPE 102492
“Monte Carlo Simulations entails generating a large number of equally likely random realizations of the reservoir fields with parameter statistics derived from sampling, solving deterministic flow equations for each realization, and post-processing the results over all realizations to obtain sample moments of the solution. This approach has the advantages of applying to a broad range of both linear and nonlinear flow problems, but has a number of potential drawbacks … the computation effort for each realization is usually large, especially for large-scale reservoirs. As a result, a detailed assessment of the uncertainty associated with flow performance predictions is rarely performed.”
“Accurate, Efficient Quantification of Uncertainty for Flow in Heterogeneous Reservoirs Using the KLME Approach.” Z. Lu, Los Alamos Natl. Laboratory; D. Zhang, U. of Oklahoma. SPE 93452, SPE Reservoir Simulation Symposium, 31 January-2 February, The Woodlands, Texas.
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10Shahab D. Mohaghegh, WVU, ISI SPE 102492
Objective
Quantify uncertainties associated with reservoir simulation studies, using Monte Carlo Simulation method.Develop a Surrogate Reservoir Model (SRM) based on a Full Field Model (FFM) for a giant oil field in the Middle East for Analysis of uncertainty.
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11Shahab D. Mohaghegh, WVU, ISI SPE 102492
Methodology
Develop an SRM based on the Full Field model.Calibrate and validate the SRM.Select KPIs for uncertainty analysis.Assign PDF to each KPI.Perform Monte Carlo Simulation.
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12Shahab D. Mohaghegh, WVU, ISI SPE 102492
FFM Characteristics
Full 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.
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13Shahab D. Mohaghegh, WVU, ISI SPE 102492
SRM Characteristics
Accurate 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.
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Very Complex Geology
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15Shahab D. Mohaghegh, WVU, ISI SPE 102492
Very Complex Geology
Reservoirs represented in the FFM.
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16Shahab D. Mohaghegh, WVU, ISI SPE 102492
Curse of Dimensionality
Source of dimensionality:STATIC: Representation of reservoir properties associated with each well.DYNAMIC: Simulation runs to demonstrate well productivity.
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17Shahab D. Mohaghegh, WVU, ISI SPE 102492
Curse of Dimensionality
Representing reservoir properties for horizontal wells.
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18Shahab D. Mohaghegh, WVU, ISI SPE 102492
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
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19Shahab D. Mohaghegh, WVU, ISI SPE 102492
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
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20Shahab D. Mohaghegh, WVU, ISI SPE 102492
Curse of Dimensionality, Static
Total number of parameters that need representation during the modeling process:
12 parameters x 40 grid block/well = 480
16 parameter per well
Total of 496 parameter per well
Building a model with 496 parameters per well is not realistic, THE CURSE OF DIMENSIONALITY
Dimensionality Reduction becomes a vital task.
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21Shahab D. Mohaghegh, WVU, ISI SPE 102492
Curse of Dimensionality, Dynamic
Well productivity is identified through following simulation runs:
All wells producing at 1500, 2500, 3500, & 4500 bpd (nominal rates)
Cap the field productivityNo cap on field productivity
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22Shahab D. Mohaghegh, WVU, ISI SPE 102492
Curse of Dimensionality, Dynamic
Well productivity through following simulation runs:
Step up the rates for all wellsCap the field productivityNo cap on field productivity
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23Shahab D. Mohaghegh, WVU, ISI SPE 102492
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. !!!
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24Shahab D. Mohaghegh, WVU, ISI SPE 102492
Curse of Dimensionality
To address this issue, we use Fuzzy Pattern Recognition technology, based on Fuzzy Cluster Analysis.
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25Shahab D. Mohaghegh, WVU, ISI SPE 102492
Parameter: Pressure @ Reference
Key Performance Indicator
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26Shahab D. Mohaghegh, WVU, ISI SPE 102492
Key Performance Indicator
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27Shahab D. Mohaghegh, WVU, ISI SPE 102492
Please Note: The lower the bar, the higher the influence.
Key Performance Indicators
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28Shahab D. Mohaghegh, WVU, ISI SPE 102492
Surrogate Modeling
40% of data was set aside as blind (verification) data.
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29Shahab D. Mohaghegh, WVU, ISI SPE 102492
Surrogate Modeling
40% of data was set aside as blind (verification) data.
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30Shahab D. Mohaghegh, WVU, ISI SPE 102492
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.
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31Shahab D. Mohaghegh, WVU, ISI SPE 102492
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.
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32Shahab D. Mohaghegh, WVU, ISI SPE 102492
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
Analysis of Uncertainty
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33Shahab D. Mohaghegh, WVU, ISI SPE 102492
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.
Analysis of Uncertainty
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34Shahab D. Mohaghegh, WVU, ISI SPE 102492
I-12
P-16
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35Shahab D. Mohaghegh, WVU, ISI SPE 102492
Key Performance Indicators
40 producing layers.
One million grid blocks.
This is the distribution of the parameter being studied in the geologic model.
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36Shahab D. Mohaghegh, WVU, ISI SPE 102492
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37Shahab D. Mohaghegh, WVU, ISI SPE 102492
Cumulative Oil ProductionUniform Distribution was assigned to the top 5 KPIs.
Gaussian Distribution was assigned to the top 5 KPIs.
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38Shahab D. Mohaghegh, WVU, ISI SPE 102492
Instantaneous Water CutUniform Distribution was assigned to the top 5 KPIs.
Gaussian Distribution was assigned to the top 5 KPIs.
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39Shahab D. Mohaghegh, WVU, ISI SPE 102492
B-91
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40Shahab D. Mohaghegh, WVU, ISI SPE 102492
Cumulative Oil Production
Influence of uncertainties associated with top and low ranking KPIs on the well
output.
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41Shahab D. Mohaghegh, WVU, ISI SPE 102492
Instantaneous Water Cut
Influence of uncertainties associated with top and low ranking KPIs on the well
output.
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42Shahab D. Mohaghegh, WVU, ISI SPE 102492
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43Shahab D. Mohaghegh, WVU, ISI SPE 102492
CONCLUSIONS
A 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.
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44Shahab D. Mohaghegh, WVU, ISI SPE 102492
CONCLUSIONS
The surrogate reservoir model would provide results in real time.The surrogate model was used to analyze uncertainties associated with the full field flow model.
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45Shahab D. Mohaghegh, WVU, ISI SPE 102492
CONCLUSIONS
Development 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.
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46Shahab D. Mohaghegh, WVU, ISI SPE 102492
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47Shahab D. Mohaghegh, WVU, ISI SPE 102492
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48Shahab D. Mohaghegh, WVU, ISI SPE 102492
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49Shahab D. Mohaghegh, WVU, ISI SPE 102492
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50Shahab D. Mohaghegh, WVU, ISI SPE 102492
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51Shahab D. Mohaghegh, WVU, ISI SPE 102492
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52Shahab D. Mohaghegh, WVU, ISI SPE 102492
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53Shahab D. Mohaghegh, WVU, ISI SPE 102492
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54Shahab D. Mohaghegh, WVU, ISI SPE 102492
Analysis of Uncertainty
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55Shahab D. Mohaghegh, WVU, ISI SPE 102492
Analysis of Uncertainty
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56Shahab D. Mohaghegh, WVU, ISI SPE 102492
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
Analysis of Uncertainty
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57Shahab D. Mohaghegh, WVU, ISI SPE 102492
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
Analysis of Uncertainty
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58Shahab D. Mohaghegh, WVU, ISI SPE 102492
PDF for HB001 Cumulative Oil and Cumulative Water production at the rate of 3,000 blpd cap after 20 years.
Analysis of Uncertainty
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59Shahab D. Mohaghegh, WVU, ISI SPE 102492
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.
Analysis of Uncertainty