Self-Learning Reservoir Management
- Luigi Saputelli (PDVSA/U. of Houston) | Michael Nikolaou (U. of Houston) | Michael J. Economides (U. of Houston)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- December 2005
- Document Type
- Journal Paper
- 534 - 547
- 2005. Society of Petroleum Engineers
- 5.7.2 Recovery Factors, 4.6 Natural Gas, 2.3 Completion Monitoring Systems/Intelligent Wells, 4.1.5 Processing Equipment, 5.5.2 Construction of Static Models, 5.1.2 Faults and Fracture Characterisation, 5.5.8 History Matching, 5.3.2 Multiphase Flow, 1.8 Formation Damage, 5.3.9 Steam Assisted Gravity Drainage, 5.5 Reservoir Simulation, 5.1 Reservoir Characterisation, 4.3.4 Scale, 5.4.6 Thermal Methods, 5.4.1 Waterflooding, 3.1.6 Gas Lift, 5.2.1 Phase Behavior and PVT Measurements, 5.1.1 Exploration, Development, Structural Geology, 7.1.10 Field Economic Analysis, 7.1.9 Project Economic Analysis, 5.2 Reservoir Fluid Dynamics, 5.1.5 Geologic Modeling, 5.6.4 Drillstem/Well Testing, 4.1.2 Separation and Treating, 1.2.3 Rock properties, 6.5.2 Water use, produced water discharge and disposal, 7.1.5 Portfolio Analysis, Management and Optimization, 5.6.8 Well Performance Monitoring, Inflow Performance
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In this work, we present an industrial automation framework for control andoptimization of hydrocarbon-producing fields while satisfying business andphysical constraints. The all-encompassing reservoir-management problem isdecomposed into a hierarchy of decision-making problems at different timescales.
We exemplify the proposed approach through a case study on a multiple-layerreservoir with a classical waterflood problem, in which a numerical reservoirmodel is used as a virtual field. A model-predictive control (MPC) strategy isused to regulate well and field instrumentation at economically optimal setpoints determined by an overlying supervisory control level. The studydemonstrates significant reduction in water-handling costs and increased oilrecovery.
This work is a starting point for further development in automaticintelligent reservoir technologies, which capitalize on the abilities ofpermanent instrumented wells and remotely activated downhole completions.
Reservoir management today is facing remarkable challenges in optimizingprofitability while satisfying a number of constraints (physical, financial,geopolitical, and human). To optimize profitability, engineers traditionallyhave used mathematical models, field data, and domain expertise in an effort tomake decisions about the best operating scenario. To increase the opportunitiesfor profitability by greatly increasing the volume of available field data andthe number of potential operating scenarios, the industry has recently starteddeploying sophisticated hardware for remote sensing and actuation of wells andfacilities.
However, the acquisition of domain expertise about an oil field is a lengthyand often unstructured activity that cannot be undertaken easily on acontinuous basis. In addition, because of the complexity and magnitude of anall-encompassing optimization problem for an entire oil field, decisions aremade in a fragmented way for various pieces of that oil field. The lack ofintelligent software applications exacerbates the situation. As a result, thecapabilities of new sensing and actuation hardware have not been fullyrealized, making it difficult to justify the significant cost that suchhardware imparts. In fact, it is fair to say that not much can be expected froma feedback-based decision-making loop unless all elements in the loop areproperly configured, connected, and functioning. The industry state of the artis clearly far from this ideal end.
To address the above issues, we propose a fieldwide optimization and controlframework1 with the following key features:
• It uses a hierarchy of time scales to separate the levels over whichdecision making is performed, thus rendering a complex problem solvable.
• It integrates field data for continuous learning of key reservoirfeatures, based on simplified empirical models suitable for real-timeoperations.
• It continuously optimizes reservoir performance while satisfying allbusiness and physical (surface and subsurface) constraints.
• It uses an advanced feedback-control strategy, which can be implementedeasily on field controllers at the wellhead or downhole.
• Its multiscale structure can naturally host optimization levels such asmultilateral selection, well location, and portfolio optimization.
To exemplify the above framework, we develop in this work an MPC (recedinghorizon) scheme that underlies a supervisory optimization level, which predictsthe best operating points of a hydrocarbon-producing field. The resultingstructure is a self-learning and self-adaptive scheme that optimizes multiphasefluid migration in compartmentalized reservoirs while integrating downholecompletions, wellhead restrictions, and business objectives andconstraints.
To demonstrate the capabilities of the proposed approach, we develop asoftware prototype and test it on a case study using a commercial reservoir-and well-modeling environment as a virtual reservoir. Dynamic simulations showthat the proposed strategy results in significant reduction of water injectedand produced, with a simultaneous increase in overall oil recovery. For thecase study presented, the self-learning reservoir-management strategy is ableto reduce cumulative water production by almost 80% and reduce water injectionby 55%, increasing project profitability from 13 to 55%.
In the sections that follow, we first give a background of currentreservoir-management challenges as applied to continuous oilfield modeling anddecision-making processes, multivariable optimization, and automatic control.We then present our proposed approach based on petroleum-system identification,an MPC strategy, and a closed-loop linear programming-optimization level thatsearches continuously for the best operating point of the field. Finally, weoffer suggestions for further work.
|File Size||2 MB||Number of Pages||14|
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