development of surrogate reservoir models (srm) for fast

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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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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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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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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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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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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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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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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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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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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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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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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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Very Complex Geology

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Reservoirs represented in the FFM.

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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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Curse of Dimensionality, Static

Representing reservoir properties for horizontal wells.

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

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Parameter: Pressure @ Reference

Key Performance Indicator

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Key Performance Indicator

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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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Key Performance Indicators

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

Key Performance Indicators

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Candidate Selection

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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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Surrogate Modeling

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Surrogate Modeling

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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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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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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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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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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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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


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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.


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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.


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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


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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


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

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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.


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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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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

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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 fast

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