Soft Computing for Reservoir Characterization

Abstract

As our problems become too complex to rely only on one discipline and as we find ourselves at the midst of information explosion multi-disciplinary analysis methods and data mining approaches in the petroleum industry become more of a necessity than professional curiosity. To tackle difficult problems ahead of us, we need to bring down the walls we have built around traditional disciplines such as petroleum engineering, geology, geophysics and geochemistry, and embark on true muti-disciplinary solutions. Our data, methodologies and workflow will have to cut across different disciplines. As a result, today’s “integration” which is based on integration of results will have to give way to a new form of integration, that is, discipline integration. In addition, to solve our complex problems we need to go beyond standard mathematical techniques. Instead, we need to complement the conventional analysis methods with a number of emerging methodologies and soft computing techniques such as Expert Systems, Artificial Intelligence, Neural Network, Fuzzy Logic, Genetic Algorithm, Probabilistic Reasoning, and Parallel Processing techniques. Soft computing differs from conventional (hard) computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty, and partial truth. Soft Computing is also tractable, robust, efficient and inexpensive. In this overview paper, we highlight role of Soft Computing techniques for intelligent reservoir characterization and exploration, seismic data processing and characterization, well logging, reservoir mapping and engineering. Reservoir characterization plays a crucial role in modern reservoir management. It helps to make sound reservoir decisions and improves the asset value of the oil and gas companies. It maximizes integration of multi-disciplinary data and knowledge and improves the reliability of the reservoir predictions. The ultimate product is a reservoir model with realistic tolerance for imprecision and uncertainty. Soft computing aims to exploit such a tolerance for solving practical problems. In reservoir characterization, these intelligent techniques can be used for uncertainty analysis, risk assessment, data fusion and data mining which are applicable to feature extraction from seismic attributes, well logging, reservoir mapping and engineering. The main goal is to integrate soft data such as geological data with hard data such as 3D seismic and production data to build a reservoir and stratigraphic model. While some individual methodologies (esp. neurocomputing) have gained much popularity during the past few years, the true benefit of soft computing lies on the integration of its constituent methodologies rather than use in isolation.Future research should focus on the integration of data and disciplinary knowledge for improving our understanding of reservoir data and reducing our prediction uncertainty.KeywordsNeural NetworkFuzzy LogicSeismic DataFuzzy RuleSoft ComputingThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.