Future Applications of Expert Systems for the Evaluation of Energy Resources
Authors: Miller Betty M. 
Published: December 2007

The loss of professional experience and expertise in the domain of the earth sciences may prove to be one of the most serious outcomes of the boom-and-bust cyclic nature of the volatile energy and mining industries. Promising new applications of powerful computer systems, known as "expert systems" or "knowledge-based systems," are predicted for use in the earth sciences. These systems have the potential capability to capture and preserve the invaluable knowledge bases essential to the evaluation of U.S. energy and mineral resources.

Introduction

A popular television commercial sponsored by an educational organization states that "a mind is a terrible thing to waste." It should be viewed as an even greater tragedy to lose the products of such a mind once it has been trained and has gained invaluable experience over the years in a specific domain of expertise. The loss of professional expertise in geology, geophysics, engineering geochemistry and log analysis may well prove to be the most serious outcome of the boom-and-bust cyclic nature of the volatile energy and mining industry.

The invaluable product lost is the knowledge base these departed professionals have amassed in their respective areas of expertise in research and industry, as opposed to structured information data bases. (The distinction between knowledge bases and information data bases will be discussed later in this paper.) Many good earth scientists are currently losing their jobs. Some are taking retirement or reassignment to other projects, and some are being forced to accept jobs in other professions. Major aspects of the knowledge compiled and assimilated, ideas and concepts inferred, interpretations and conclusions projected-all a part of the decision making process in the research, exploration, and development of energy and mineral resources-will be lost as a national asset for the continuing inventory and development of the nation's resources. If the various academic spokesmen are correct in their predictions that declining enrollment in college and university geology and geophysics departments around the country has reached a level that threatens the future of the earth science profession. then we also are faced with the loss of this knowledge base without significant replacement in the near future.

This is only one of the challenges facing the volatile resource industries efforts to deal with the current unprecedented boom-and- bust cycle and its long-term effects on the nation's energy future. Industry's research, exploration, and development budgets are declining because of cash flow problems. The number of wells being drilled in the U.S. has declined to the level of pre-World War II days, and petroleum reserves will decline accordingly. The diversified infrastructures of many of the energy companies are being consolidated or have been merged into fewer large companies with the inevitable reductions and losses in professional staffs.

However, some impressive technological gains continue to be made during these troubled times, particularly in computer-oriented industries, that should be considered in long-term strategies for the resource industry to address the current issue of this loss of professional experience and expertise. This costly and invaluable geologic knowledge provides a basic asset for the energy and mining industries now and for the future. (The general expression "geology" or "geologic knowledge" is used to avoid redundancy in naming each time all the special areas of expertise in the field of the earth sciences.) It is this issue, the preservation of resource expertise through the application of potentially new and challenging computer technology borrowed from the field of applied artificial intelligence, that is discussed in this paper.

Background

In recent years, research in the field of applied artificial intelligence (AI) has achieved considerable success. Among the most significant of these Al successes that have attracted both business and research interests since the mid-1960's is the development of powerful new computer systems known as "expert systems" or "knowledge- based systems." These programs are designed to represent and to apply factual knowledge and rules drawn from human experts in specific areas of expertise to solve problems. This area of Al research concentrates on the construction of high-performance hard- ware and software using symbolic programming that seeks to replicate the knowledge. reasoning, and linguistic skills of people in specialized professional domains. These knowledge-based systems are different from conventional programming techniques used to create the large data processing systems we commonly associate with computers. Conventional systems, by means of complex algorithms, are capable of collecting and processing large volumes of factual data to build information data banks, as opposed to knowledge-based systems that combine facts, specialized knowledge, and the judgment of an expert as encoded or translated into an expert-systems program.

Numerous uses for these knowledge-based expert systems are now considered feasible for many domains of expertise. However, these systems are not now in wide use (although this is rapidly changing), or they are not in the public domain. Among other purposes are expert systems that help financial analysts invest money and engineers design products. Some of the better-known expert systems have been used for diagnostic tools in the medical profession, e.g., INTERNIST and MYCIN; chemical analyses, e.g., DENDRAL; criminal investigations by the FBI, e.g., LITTLE FLOYD; various applications in military strategy; exploration tools in mineral prospecting, e.g., PROSPECTOR; and Schlumberger's well log analysis, e. g., DIPMETER ADVISOR. Several other known earth science applications for expert systems are Baroid's MUDMAN, used for diagnosing drilling problems and drilling mud applications; Amoco's PROBWELL, used to diagnose problems with producing oil wells; and several prototypical expert systems constructed in the U.S. Geological Survey (USGS) for the microcomputer to classify sedimentary basins for petroleum resource assessment called muPETROL and a microversion of PROSPECTOR called muPROSPECTOR. In total, however, there are very few expert systems currently being applied in the earth sciences that are available for public use.

Knowledge-Based Systems.

Knowledge in most specialties is usually from two sources: public and private. Public knowledge includes the published definitions, facts, and theories of which textbooks and references are typically composed in the domain of study. But expertise usually involves more than just public knowledge. Experts, particularly in research and industry, generally have access to confidential records and possess private or confidential knowledge that has not found its way into the published literature. This confidential or unpublished knowledge from the experts consists

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