Reservoir Modeling by Data Integration via Intermediate Spaces and Artificial Intelligence Tools in MPS Simulation Frameworks
- Amirkabir University of Technology, PhD Student at Reservoir Engineering, Department of Petroleum Engineering (Iran, Islamic Republic of)
- Amirkabir University of Technology, Faculty of Petroleum Engineering (Iran, Islamic Republic of)
Conditioning stochastic simulations are very important in many geostatistical applications that call for the introduction of nonlinear and multiple-point data in reservoir modeling. Here, a new methodology is proposed for the incorporation of different data types into multiple-point statistics (MPS) simulation frameworks. Unlike the previous techniques that call for an approximate forward model (filter) for integration of secondary data into geologically constructed models, the proposed approach develops an intermediate space where all the primary and secondary data are easily mapped onto. Definition of the intermediate space, as may be achieved via application of artificial intelligence tools like neural networks and fuzzy inference systems, eliminates the need for using filters as in previous techniques. The applicability of the proposed approach in conditioning MPS simulations to static and geologic data is verified by modeling a real example of discrete fracture networks using conventional well-log data. The training patterns are well reproduced in the realizations, while the model is also consistent with the map of secondary data.
- OSTI ID:
- 22210454
- Journal Information:
- Natural Resources Research (New York, N.Y.), Vol. 22, Issue 4; Other Information: Copyright (c) 2013 International Association for Mathematical Geology; http://www.springer-ny.com; Country of input: International Atomic Energy Agency (IAEA); ISSN 1520-7439
- Country of Publication:
- United States
- Language:
- English
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