Intelligent Reservoir Characterization

Abstract

Data integration, risk assessment and quantification of uncertainty are key issues in petroleum exploration and development. In recent years there has been tremendous efforts to find new methods to address these issues. As the problems become too complex and the cost associated with poor predictions (e.g. dry holes) increases, the need for proper integration of disciplines, data fusion, risk reduction and uncertainty management become more important. Soft computing methods such as neural networks, fuzzy logic, evolutionary computing, and probabilistic reasoning approaches offer an excellent opportunity to address the issues such as integrating information from various sources with varying degrees of uncertainty, establishing relationships between measurements and reservoir properties, and assigning risk factors or error bars to predictions. Deterministic model building and interpretation are increasingly replaced by stochastic and soft computing-based methods. The diversity of soft computing applications in oil field problems and prevalence of their acceptance are manifested by the overwhelming interest in the topics covered in this book. Future research should focus on the integration of data and disciplinary knowledge for improving our understanding of reservoir data and reducing our prediction uncertainty. Hybrid intelligent systems will provide such tools for future multidisciplinary studies.