Past, present and future intelligent reservoir characterization trends

As we approach the next millennium, and as our problems become too complex to rely only on one discipline to solve them more effectively, multi-discip linary approaches in the petroleum industry become more of a necessity than professional curiosity. We will be forced to bring down the walls we have built around classical disciplines such as petroleum engineering, geology, geophysics and geochemistry, or at the very least, make them more permeable . Our data, methodologies and approaches to tackle problems will have to cu t across various disciplines. As a result, today's "integration", which is based on integration of results, will have to give way to a new form of int egration, that is, integration of disciplines. In addition, to solve our co mplex problem, one needs to go beyond standard techniques and silicon hardw are. The model needs to use several emerging methodologies and soft computi ng techniques: Expert Systems, Artificial Intelligence, Neural Network, Fuz zy Logic (GL), Genetic Algorithm (GA), Probabilistic Reasoning (PR), and Pa rallel Processing techniques. Soft computing differs from conventional (har d) 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 paper, we reveal (explore) the role of S oft Computing techniques in intelligent reservoir characterization and expl oration. (C) 2001 Elsevier Science B.V. All rights reserved.