Integrated Asset Modeling through Optimization of Multiple Reservoirs Using Next-Generation Reservoir Simulators
- Cenk Temizel (Aera Energy) | Bao Jia (University of Kansas) | Rahul Ranjith (University of Southern California) | Anuj Suhag (University of Southern California) | Karthik Balaji (University of Southern California) | Dike Putra (Rafflesia Energy) | Onder Saracoglu (Consultant)
- Document ID
- Society of Petroleum Engineers
- SPE Europec featured at 79th EAGE Conference and Exhibition, 12-15 June, Paris, France
- Publication Date
- Document Type
- Conference Paper
- 2017. Society of Petroleum Engineers
- 4.1.2 Separation and Treating, 3.2 Well Operations and Optimization, 3 Production and Well Operations, 4 Facilities Design, Construction and Operation, 5.5 Reservoir Simulation, 7 Management and Information, 7.1.7 Intergated Asset Management, 4.1 Processing Systems and Design, 7.1.5 Portfolio Analysis, Management and Optimization, 5 Reservoir Desciption & Dynamics, 3.2.7 Lifecycle Management and Planning, 7.1 Asset and Portfolio Management
- next-generation reservoir simulators, optimization, integrated asset management, simulation of multiple reservoirs
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Integrated asset modeling (IAM) offers the oil industry several benefits. Next-generation reservoir simulators help achieve faster runtimes, insight into interaction between various components of a development, can be used as an effective tool in detecting bottlenecks in a production system, and as a constant and more effective communication tool between various departments. IAM provides significant opportunities for optimization of very large or complex infrastructures, and life-of-field analysis of production optimization scenarios.
Simultaneous modeling of surface and subsurface components helps reduce time and enhances efficiency during the decision-making process, which eliminates the requirement for tedious, timeconsuming work and iterations between separate solutions of reservoir and surface networks. Beyond this convenience, the technology makes it possible to reach more robust results quicker using surface- subsurface coupling. The objective of this study is to outline the advantages and challenges in using next-generation simulators on simulation of multiple reservoirs in integrated asset management.
Simultaneous simulation of multiple reservoirs adds another dimension of complexity to the process of IAM. Several sub-reservoir models can be simulated simultaneously in large fields comprising subreservoirs with complex surface systems, which could otherwise become very tedious to handle. In this study, a next-generation reservoir simulator is coupled with an optimization and uncertainty tool that is used to optimize net present value of the entire asset. Several constraints and bottlenecks in such a large system exist, all connected to one another. IAM proves useful in debottlenecking to increase efficiency of the thorough system. The strengths and difficulties associated with simultaneous simulation and optimization of multiple reservoirs are compared to the conventional way of simulating assets separately, thus illustrating the benefits of using next-generation reservoir simulators during optimization of multiple reservoirs.
The results show that simultaneous solution of the surface-subsurface coupling gives significantly faster results than a system that consists of separate solutions of surface and subsurface. This difference in speed becomes more significant when the number of reservoirs simulated is greater than one. This study outlines the workflow in setting up the model, CPU time for each component of simulation, and the explanation of each important item in this process, to illustrate the incremental benefits of use of next-generation reservoir simulators in simulating multiple reservoirs with surface facilities taken into account.
Although the use of next-generation simulators is becoming more common, solid examples that illustrate the benefits of simultaneous simulation of multiple reservoirs with surface facilities under several different constraints like this study, are important to prove the use of such tools where it is convenient to carry out the optimization in a system that handles decision parameters and constraints simultaneously.
|File Size||1 MB||Number of Pages||13|
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