An overview of current developments in the application of expert systems to the geologic analysis of sedimentary basins is presented. Techniques are adapted from the field of Artificial Intelligence (AI) and are interfaced with computer mapping techniques using knowledge-based Geographic Information Systems (GIS).
The concepts and methods for analyzing sedimentary basins in petroleum exploration have changed from fairly simplistic, geologic studies, which employed primarily qualitative and semiquantitative techniques, to studies of ever increasing complexity, which employ quantitative evaluations of total basin systems within a three-dimensional (3-D) framework. Enormous amounts of multivariate spatial data are necessary to quantify the geologic, geophysical, geochemical, and hydrologic processes within complex sedimentary basins.
Such an integrated analysis for a sedimentary basin is difficult to achieve without computer assistance. New applications of artificial intelligence and expert systems techniques can, however, be interfaced with knowledge-based GIS to provide the tools needed to define new strategies and technologies for conducting and automating the complex tasks required for geologic studies of sedimentary basins and particularly for 3-D basin analysis.
Research efforts in the U.S. Geological Survey are currently being directed at exploring the feasibility of applying expert systems and knowledge acquisition techniques to the design and construction of a global system of sedimentary basin classification and at the geologic analysis of sedimentary basins to assess their petroleum potential. The primary objective is to design a prototype expert system and a knowledge-based GIS that capture both the logic used to define the geologic concepts and the reasoning that enables the geologist to understand and reconstruct the geologic evolution of a sedimentary basin. The system provides these capabilities through documentation of its major components, as expressed, for example, by its stratigraphy, structural geology, and sedimentology. This expert system is designed to analyze the traditional concepts of source, reservoir, and trapping mechanism; to aid in the diagnosis of geologic conditions favorable for the occurrence of petroleum or other energy resources; and, ultimately, to assess these resources.
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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.