Development of Surrogate Reservoir Models (SRM) For Fast Track Analysis of Complex Reservoirs
- Shahab D. Mohaghegh (West Virginia U.) | Cyrus Abdallah Modavi (Abu Dhabi Co. Onshore Oil Opn.) | Hafez H. Hafez (Abu Dhabi Co. Onshore Oil Opn.) | Masoud Haajizadeh (BP Amoco PLC) | Maher Mahmoud Kenawy (Geisum Oil Co.) | Srikanth Guruswamy (Abu Dhabi Co. Onshore Oil Opn.)
- Document ID
- Society of Petroleum Engineers
- Intelligent Energy Conference and Exhibition, 11-13 April, Amsterdam, The Netherlands
- Publication Date
- Document Type
- Conference Paper
- 2006. Society of Petroleum Engineers
- 7.6.6 Artificial Intelligence, 5.5.8 History Matching, 5.1.5 Geologic Modeling, 4.3.4 Scale, 5.1.2 Faults and Fracture Characterisation, 2.3 Completion Monitoring Systems/Intelligent Wells, 4.1.2 Separation and Treating, 5.4.2 Gas Injection Methods, 3.3.1 Production Logging, 5.5 Reservoir Simulation, 5.1 Reservoir Characterisation, 5.4.7 Chemical Flooding Methods (e.g., Polymer, Solvent, Nitrogen, Immiscible CO2, Surfactant, Vapex), 3.2.3 Hydraulic Fracturing Design, Implementation and Optimisation, 5.3.9 Steam Assisted Gravity Drainage
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Reservoir simulation has become the industry standard for reservoir management. It is now used in all phases of field development in the oil and gas industry. The full field reservoir models that have become the major source of information and prediction for decision making are continuously updated and major fields now have several versions of their model with each new version being a major improvement over the previous one. The newer versions have the latest information (geologic, geophysical and petro-physical measurements, interpretations and calculations based on new logs, seismic data, injection and productions, etc.) incorporated in them along with adjustments that usually are the result of single-well or multi-well history matching.
A typical reservoir model consists of hundreds of thousands and in many cases millions of grid blocks. As the size of the reservoir models grow the time required for each run increases. Schemes such as grid computing and parallel processing helps to a certain degree but cannot close the gap that exists between simulation runs and real-time processing. On the other hand with the new push for smart fields (a.k.a. i-fields) in the industry that is a natural growth of smart completions and smart wells, the need for being able to process information in real time becomes more pronounced. Surrogate Reservoir Models (SRMs) are the natural solution to address this necessity. SRMs are prototypes of the full field models that can run in fractions of a second rather than in hours or days. They mimic the capabilities of a full field model with high accuracy. These models can be developed regularly (as new versions of the full field models become available) off-line and can be put online for automatic history matching and real-time processing that can guide important decisions. SRMs can efficiently be used for real-time optimization, real-time decision making as well as analysis under uncertain conditions.
This paper presents a unified approach for development of SRMs using the state-of-the-art in intelligent systems techniques. An example for developing an SRM for a giant oil field in the Middle East is presented and the results of the analysis using the SRM for this field is discussed. In this example application SRM is used in order to analyze the impact of the uncertainties associated with several input parameters into the full field model.
Over the past several years computer simulation has made major advances in terms of scope and complexity. Today they can reach the levels of accuracy, which make it possible to play realistic scenarios of complex mechanical and geo-physical processes. The success of computer simulation techniques is due to the development of efficient algorithms and solution methods for general partial differential equations (PDE), the advancement of modern computational fluid dynamic (CFD) and multi-physics simulation technologies, as well as due to the availability of increasingly capable hardware platforms, such as supercomputer facilities, and Beowulf clusters.
Reservoir Simulation is now an industry standard. No serious alternative to the conventional reservoir simulation and modeling is in the horizon. It is a well understood technology that usually works well in the hand of experience modelers incorporating reasonably good geological, geophysical, and petro-physical interpretations and measurements with the reasonably sophisticated simulators that are currently available in the market. The reservoir models that are built for an average size field with tens and sometimes hundreds of wells tend to include very large number of grid blocks. As the number of reservoir layers or the thickness of the formations increase the number of cells included in the model approaches several millions. Technologies such as Local Grid Refinements1-2 have been developed to dampen the geometric increase of the number of grid blocks required for detail and focused simulation and modeling around the wellbore and locations in the reservoir where more detail is required, but the size of the models remains in the several millions of cells.
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