6: Knowledge-Based Reasoning in SEISIS—A Rule-Based System for Interpretation of Seismic Sections Based on Texture
Zhen Zhang, M. Simaan, 1991. "Knowledge-Based Reasoning in SEISIS—A Rule-Based System for Interpretation of Seismic Sections Based on Texture", Expert Systems in Exploration, Fred Aminzadeh, Marwan Simaan
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SEISIS is a knowledge-based system for the automatic segmentation of seismic sections into large regions of common textural properties. Such regions are believed to contain geologic information which can be related to large scale tectonic events, such as salt diapirs and shale ridges, or to different depositional environments of the constituent sediments. Human expert knowledge is introduced in SEISIS in order to resolve uncertainties in the numerical data and to help in making segmentation decisions. This domain-dependent knowledge, however, may be stated using terms having imprecise or fuzzy meanings, such as “unlikely”, “usually”, “seldom”, etc. Furthermore, the conditions in the IF part of expert rules normally refer to and should be matched with the information/facts collected during the segmentation process. These facts generally are associated with certain types of “uncertainties” to reflect their relative truthfulness. How to integrate all this information and knowledge, which is typically of diverse sources and with different scales, to reach a final classification decision is a crucial problem in the development and actual implementation of SEISIS’s knowledge-based segmentation process. We shall discuss in detail the knowledge-based reasoning process used in SEISIS. We use a small piece of a stacked seismic section from the Gulf of Mexico and a sample expert rule relevant to that particular section to demonstrate (1) the actual numerical and symbolic computations involved in SEISIS, and (2) the integration of information and knowledge using a Certainty Factor vector updating procedure with a probability based plausible reasoning model.
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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.