SPE DISTINGUISHED LECTURER SERIES is funded principally through a grant of the SPE FOUNDATION The Society gratefully acknowledges those companies that support the program by allowing their professionals by allowing their professionals to participate as Lecturers. Shahab D. Mohaghegh, Ph.D. – WVU & ISI SPE Distinguished Lecture Series, 2007 - 2008 And special thanks to The American Institute of Mining, Metallurgical, and Petroleum Engineers (AIME) for their contribution to the program.
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SPE DISTINGUISHED LECTURER SERIESNo cap on field productivity (1 simulation runs)No cap on field productivity (1 simulation runs) Cap the field productivity (1 simulation runs) Shahab
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And special thanks to The American Institute of Mining, Metallurgical,
and Petroleum Engineers (AIME) for their contribution to the program.
Smart Completions, Smart Wells and SPE Distinguished Lecture 2007-2008
Now Smart Fields; Challenges & Potential SolutionsChallenges & Potential Solutions
Shahab D. Mohaghegh, Ph.D.West Virginia University &g yIntelligent Solutions, Inc.
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Smart Oil Field Technology
Smart Completion:Smart Completion:Remotely monitor & control downhole fluid production or injection.Downhole control to adjust flow distributionsadjust flow distributions along the wellbore to correct undesirable fl id f t tfluid front movement.
Smart Well:Using permanent downhole gauges for continuous monitoring of pressure, flow rates, … and automatic fl t lflow controls.Capability of automatic interaction using extensive downhole communication.
Intelligence requires a combination of hardware and software.We have made strong advances in hardware.gSoftware development is lagging.Intelligent Systems will play a pivotal role:Intelligent Systems will play a pivotal role:
Artificial Neural NetworksFuzzy Set TheoryFuzzy Set TheoryGenetic Optimization
Surrogate Reservoir Models are replicas ofSurrogate Reservoir Models are replicas of the numerical simulation models (full field flow models) that run in real-timeflow models) that run in real time.REPLICA.
A d ti f k f t i llA copy or reproduction of a work of art, especially one made by the original artist.A copy or reproduction especially one on a scaleA copy or reproduction, especially one on a scale smaller than the original.Something closely resembling another.
SRMs are notSRMs are notresponse surfaces.statistical representations of simulation modelsstatistical representations of simulation models.
SRMs areengineering toolsengineering tools honor the physics of the problem in hand.adhere to the definition of “System Theory”adhere to the definition of System Theory .
Lets see an example of a SurrogateLets see an example of a Surrogate Reservoir Model in action.This case study demonstrates developmentThis case study demonstrates development of a surrogate reservoir model (SRM) that will run in real-time in order to accomplish therun in real time in order to accomplish the objectives of the project.
A giant oil field in the Middle EastA giant oil field in the Middle East.Complex carbonate formation.165 horizontal wells165 horizontal wells.Total field production capped at 250,000 BOPD.Each well is capped at 1,500 BOPD.Water injection for pressure maintenance.
Management Concerns:Management Concerns:Water production is becoming a problem.Cap well production to avoid bypass oilCap well production to avoid bypass oil.Uncertainties associated with models.
Technical Team’s Concerns:Technical Team s Concerns:May be able to produce more oil from some wells (which ones? How much increase?) without ( )significant increase in water cut.Increasing well rate may actually help recovery.
Accomplishing this objective requires:Accomplishing this objective requires:Exhaustive search of the solution space, examining all possible production scenarios, while considering uncertainties associated with the geological modeluncertainties associated with the geological model.Hundreds of thousands of simulation runs; thus development of a Surrogate Reservoir Model (SRM) based on the Full Field Model (FFM) became a requirement.
Full Field Flow Model Characteristics:Full Field Flow Model Characteristics:Underlying Complex Geological Model.Industry Standard Commercial Reservoir Si l tSimulator165 Horizontal Wells.Approximately 1,000,000 grid blocks.Approximately 1,000,000 grid blocks.Single Run = 10 Hours on 12 CPUs.
OUTPUT was identified by the ObjectiveOUTPUT was identified by the ObjectiveCumulative Oil ProductionCumulative Water ProductionCumulative Water ProductionInstantaneous Water Cut
INPUT must be designed in a way to captureINPUT must be designed in a way to capture the complexity of the reservoir.
Well-based SRMWell based SRMWell-based SRM gridCurse of dimensionality
Complexity of a system increases with itsComplexity of a system increases with its dimensionality.Tracking system behavior becomesTracking system behavior becomes increasingly difficult as the number of dimensions increases.dimensions increases.Systems do not behave in the same manner in all dimensionsin all dimensions.
Sources of dimensionality:Sources of dimensionality:STATIC: Representation of reservoir properties associated with each well.DYNAMIC: Simulation runs to demonstrate well productivity.
Need to understand reservoir’s response to changes in imposed constraints.
Curse of Dimensionality, Dynamic
Well productivity through following i l tisimulation runs:
Step up the rates for all wellsNo cap on field productivity (1 simulation runs)No cap on field productivity (1 simulation runs)Cap the field productivity (1 simulation runs)
Need to understand reservoir’s response to changes in imposed constraints.
Data Generation
Total of 10 simulation runs were made to t th i d t t f th SRMgenerate the required output for the SRM
development (training, calibration & validation)validation)Using Fuzzy Pattern Recognitiontechnology input to the SRM was compiledtechnology input to the SRM was compiled.
I d t dd th “C fIn order to address the “Curse of Dimensionality” one must understand the behavior and contribution of each of thebehavior and contribution of each of the parameters to the process being modeled.Not a simple and straight forward task !!!Not a simple and straight forward task. !!!
Identify wells that benefit from a rate increaseIdentify wells that benefit from a rate increase and those that would not.Address the uncertainties associated with theAddress the uncertainties associated with the simulation model.Generate Type curves for each wellGenerate Type curves for each well.
Design production strategy.Use as assisted history matching toolUse as assisted history matching tool.
To perform the above analyses millions of simulation runs were required.
Wells were divided into 5 clustersWells were divided into 5 clusters.Production in wells in cluster 1 can be increased significantly without substantialincreased significantly without substantial increase in water production.
Motivation:Motivation:The Full Field Model is a reservoir simulator that is based on a geologic model. g gThe geologic model is developed based on a set of measurements (logs, core analysis, seismic, …) and corresponding geological and geophysical interpretations.
Motivation:Motivation:Therefore, like any other reservoir simulation and modeling effort, it includes certain obvious g ,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)behavior in the horizontal wells in this field (KPIs).
Following are the steps involved:Following are the steps involved:1. Identify a set of key performance indicators that
are most vulnerable to uncertainty.y2. Define probability distribution function for each of
the performance indicators.a. Uniform distributionb. Normal (Gaussian) distributionc Triangular distributionc. Triangular distributiond. Discrete distribution
Following are steps involved:Following are steps involved:3. Run the neural network model hundreds or
thousands of times using the defined probability g p ydistribution functions for the identified reservoir parameters. Performing this analysis using the act al F ll Field Model is impracticalactual Full Field Model is impractical.
4. Produce a probability distribution function for cumulative oil production and the water cut atcumulative oil production and the water cut at different time and liquid rate cap.
Type curves can be generated in secondsType curves can be generated in seconds to address sensitivity of oil and water production to all involved parameters.p pType curves can be generated for:
Individual wellsIndividual wellsEach cluster of wellsEntire fieldEntire field
Upon completion of the project managementUpon completion of the project management allowed production increase in six cluster one wells.After 8 months of successful production rest of the cluster one wells were also put onof the cluster one wells were also put on higher production.It has been more than 15 months since theIt has been more than 15 months since the results were implemented with success.
A successful surrogate reservoir model wasA 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 flowmimic the behavior of the actual full field flow model in real-time.
Development of successful surrogateDevelopment of successful surrogate reservoir model is an important and essential step toward development of next generation p p gof reservoir management tools that would address the needs of smart fields.