Expert systems, though not widely used in the oil industry, have been the object of a large volume of research and development activities in the industry and in academia. Two reasons for limited practical usage of expert systems in oil exploration are (1) exploration in general is highly multidisciplinary, and (2) rules governing the exploration process are, for the most part, subjective. The combination of these two factors has made development of expert systems for solving practical exploration problems difficult. Recent advances in some areas of expert systems, coupled with the availability of cost effective and fast workstations, offer opportunities to overcome the two major obstacles. Specifically, using concepts such as evidential reasoning, fuzzy logic, and neural networks in expert systems makes integration of different knowledge sources, implementation of inexact and qualitative rules (information), and self learning more practical.
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“So far as the laws of mathematics refer to reality they are not certain; and so far as they are certain, they don’t refer to reality.” This quote from Albert Einstein explains the difficulties associated with a mathematical description of many subjective rules used by human experts in reaching a decision. This fact, combined with the multidisciplinary nature of oil exploration, has prevented wide spread use of expert systems in the oil industry. Alternative approaches, that can account for uncertainty and are better equipped for integrating data and rules from different disciplines, should be used to allow expert systems to become viable tools in our applications.
This book examines a diverse set of petroleum exploration problems that properly designed expert systems can help solve. Chapter 1 provides an extensive review of current state-of-the-art expert systems as pertains to oil industry problems. Emphasis is given to how uncertainty and inexactness of data and rules from different disciplines could be handled by expert systems. The chapter suggests that fuzzy logic, evidential reasoning, and neural networks will prove essential in the design of many expert systems that are capable of solving more practical exploration problems. The problem of automatic picking of stacking velocities, using a rule based system, is addressed in Chapter 2. The expert system automates the task of picking the extreme of the velocity spectrum. The system incorporates common sense rules to distinguish primary reflection related peaks in the spectrum from those related to multiples and noise.