9: A Fuzzy Expert System for Thin-Section Mineral Identification
Abstract
This paper describes a fuzzy expert system for identification of minerals in thin section. Linguistic variables and hedges, as well as fuzzy numbers, are employed to perform approximate modes of reasoning in arriving at approximate answers from an input set of incomplete and imprecise optical data obtained from minerals in thin section. The output consists of a ranked list of possible minerals. The knowledge base of this expert system, XMIN-F, consists of the optical properties of 142 minerals. The list can be expanded easily by the user through the use of a built-in editor. The program is coded in Turbo Pascal, and a user friendly environment is provided by way of numerous windows and menus.
Figures & Tables
Contents
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.