The theory of fuzzy classification with applications to geophysical data is explored. In the context of expert systems fuzzy classification helps improve the efficiency of the inference mechanism and the development of “fuzzy rules” for automatic classification. The proposed technique is motivated by the inaccuracy and unreliability that often exist in the geophysical data which adversely affect the performance of the Bayesian classifiers. Fuzzy classification, through its membership function, offers an elegant solution to the problem. The algorithm consists of supervised and unsupervised components. These two components interact in a hybrid fashion depending upon the level of uncertainty associated with the control samples. The algorithm takes into account the uncertainty in the data by assigning continuous membership grades to the samples with respect to the classes. Examples from seismic data illustrate the geophysical applications of the method.
Figures & Tables
Expert Systems in Exploration
“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.