Soft Computing for Intelligent Reservoir Characterization

Abstract

Abstract This paper presents an overview of soft computing techniques for reservoir characterization. The key techniques include neurocomputing, fuzzy logic and evolutionary computing. A number of documented studies show that these intelligent techniques are good candidates for seismic data processing and characterization, well logging, reservoir mapping and engineering. Future research should focus on the integration of data and disciplinary knowledge for improving our understanding of reservoir data and reducing our prediction uncertainty. Introduction Accurate prediction of reservoir performance is a difficult problem. This is mainly due to the failure of our understanding of the spatial distribution of lithofacies and petrophysical properties. Because of this, the recovery factors in many reservoirs are unacceptably low. The current technologies based on conventional methodologies are inadequate and/or inefficient. In this paper, we propose the next generation of reservoir characterization tools for the new millennium - soft computing1,2,3. 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. Soft computing is an ensemble of various intelligent computing methodologies which include neurocomputing, fuzzy logic and evolutionary computing. Unlike the conventional or hard computing, it is tolerant of imprecision, uncertainty and partial truth. It is also tractable, robust, efficient and inexpensive. 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. Figure 1 shows schematically the flow of information and techniques to be used for intelligent reservoir characterization4,5,6,7. 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. This paper firstly outlines the unique roles of the three major methodologies of soft computing - neurocomputing, fuzzy logic and evolutionary computing. We will summarize a number of relevant and documented reservoir characterization applications. Lastly we will provide a list of recommendations for the future use of soft computing. This includes the hybrid of various methodologies (e.g. neural-fuzzy or neuro-fuzzy, neural-genetic, fuzzy-genetic and neural-fuzzy-genetic) and the latest tool of "computing with words" (CW)8. CW provides a completely new insight into computing with imprecise, qualitative and linguistic phrases and is a potential tool for geological modeling which is based on words rather than exact numbers. An appendix is also provided for introducing the basics in soft computing.