Development of Surrogate Reservoir Models (SRM) For...
Transcript of Development of Surrogate Reservoir Models (SRM) For...
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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
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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, ….
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SPE 99667
Shahab D. Mohaghegh, Ph.D. (WVU & ISI)
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.
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SPE 99667
Shahab D. Mohaghegh, Ph.D. (WVU & ISI)
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.
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SPE 99667
Shahab D. Mohaghegh, Ph.D. (WVU & ISI)
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.
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SPE 99667
Shahab D. Mohaghegh, Ph.D. (WVU & ISI)
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)
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SPE 99667
Shahab D. Mohaghegh, Ph.D. (WVU & ISI)
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.
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SPE 99667
Shahab D. Mohaghegh, Ph.D. (WVU & ISI)
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.
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SPE 99667
Shahab D. Mohaghegh, Ph.D. (WVU & ISI)
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.
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SPE 99667
Shahab D. Mohaghegh, Ph.D. (WVU & ISI)
Surrogate Reservoir ModelsProject Objectives:
It can provide a foundation for:Analysis of Uncertainty (Monte Carlo Simulation)Real Time OptimizationReal Time Decision MakingAutomatic History Matching
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SPE 99667
Shahab D. Mohaghegh, Ph.D. (WVU & ISI)
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
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SPE 99667
Shahab D. Mohaghegh, Ph.D. (WVU & ISI)
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).
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SPE 99667
Shahab D. Mohaghegh, Ph.D. (WVU & ISI)
Very Complex Geology
Reservoirs represented in the FFM.
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SPE 99667
Shahab D. Mohaghegh, Ph.D. (WVU & ISI)
Curse of DimensionalitySource of dimensionality:
STATIC: Representation of reservoir properties associated with each well.DYNAMIC: Simulation runs to demonstrate well productivity.
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SPE 99667
Shahab D. Mohaghegh, Ph.D. (WVU & ISI)
Curse of Dimensionality, Static
Representing reservoir properties for horizontal wells.
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SPE 99667
Shahab D. Mohaghegh, Ph.D. (WVU & ISI)
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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SPE 99667
Shahab D. Mohaghegh, Ph.D. (WVU & ISI)
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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SPE 99667
Shahab D. Mohaghegh, Ph.D. (WVU & ISI)
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.
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SPE 99667
Shahab D. Mohaghegh, Ph.D. (WVU & ISI)
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
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SPE 99667
Shahab D. Mohaghegh, Ph.D. (WVU & ISI)
Curse of Dimensionality, Dynamic
Well productivity through following simulation runs:
Step up the rates for all wellsNo cap on field productivityCap the field productivity
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SPE 99667
Shahab D. Mohaghegh, Ph.D. (WVU & ISI)
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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SPE 99667
Shahab D. Mohaghegh, Ph.D. (WVU & ISI)
Curse of DimensionalityTo address this issue, we use ISI’s Fuzzy Pattern Recognition technology.
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SPE 99667
Shahab D. Mohaghegh, Ph.D. (WVU & ISI)
Parameter: Pressure @ Reference
Key Performance Indicator
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SPE 99667
Shahab D. Mohaghegh, Ph.D. (WVU & ISI)
Key Performance IndicatorsPotential Benefits:
This analysis would confirm or dispute hypotheses and findings, purely based on the collected data:
GeologyPetrophysicsDynamic Data
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SPE 99667
Shahab D. Mohaghegh, Ph.D. (WVU & ISI)
Please Note: The lower the bar, the higher the influence.
Key Performance Indicators
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SPE 99667
Shahab D. Mohaghegh, Ph.D. (WVU & ISI)
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.
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SPE 99667
Shahab D. Mohaghegh, Ph.D. (WVU & ISI)
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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SPE 99667
Shahab D. Mohaghegh, Ph.D. (WVU & ISI)
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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SPE 99667
Shahab D. Mohaghegh, Ph.D. (WVU & ISI)
Analysis of Uncertainty
Objective:To address and analyze the uncertainties associated with the Full Field Model using Monte Carlo simulation method.
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SPE 99667
Shahab D. Mohaghegh, Ph.D. (WVU & ISI)
Analysis of Uncertainty
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.
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SPE 99667
Shahab D. Mohaghegh, Ph.D. (WVU & ISI)
Analysis of Uncertainty
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).
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SPE 99667
Shahab D. Mohaghegh, Ph.D. (WVU & ISI)
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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SPE 99667
Shahab D. Mohaghegh, Ph.D. (WVU & ISI)
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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SPE 99667
Shahab D. Mohaghegh, Ph.D. (WVU & ISI)
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.
Analysis of Uncertainty
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SPE 99667
Shahab D. Mohaghegh, Ph.D. (WVU & ISI)
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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SPE 99667
Shahab D. Mohaghegh, Ph.D. (WVU & ISI)
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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SPE 99667
Shahab D. Mohaghegh, Ph.D. (WVU & ISI)
Analysis of Uncertainty
PDF for HB001 Cumulative Oil and Cumulative Water production at the rate of 3,000 blpd cap after 20 years.
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SPE 99667
Shahab D. Mohaghegh, Ph.D. (WVU & ISI)
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
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SPE 99667
Shahab D. Mohaghegh, Ph.D. (WVU & ISI)
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.
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SPE 99667
Shahab D. Mohaghegh, Ph.D. (WVU & ISI)
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