A suggested rationale and selected bibliography for using rule-based expert computer systems (artificial intelligence) in recurrent petroleum resource assessments

Abstract

hydrates, and tight-gas sands, will be included for the first time in these estimates/reports. Undoubtedly, these inclusions will prove to be an added complexity and challenge to achieving consistent and creditable results. Resource assessment methodology, with or without expert systems, is not precise and probably never will be. Although the methodology is continually evolving and being refined, the work incorporates much subjective judgment and "educated guessing" about the prerequisite geologic factors needed for hydrocarbon accumulation. Undiscovered resources are continually being converted into reserves, and the steady improvements in technology are other factors influencing the validity of resource assessment estimates. Diversity of estimates over time has depended upon the methodology used (Miller, 1986b). Expert systems, however, can provide consistency by forcing the assessors to think about the empirical associations (i.e., addressing and writing the rules required in expert systems) in geology that are prerequisite to hydrocarbon accumulations in sedimentary basins. The theoretical principles of nature and geology are not the fundamental rules of a rule-based system. The inferred relationships and qualities, or "rules of thumb", among source rocks, reservoirs, traps, and timing of generation, migration, and trap formation, for example, are the bases for writing rules. Rules and empirical associations can be codified in expert systems to screen out all cases that are beyond the limits of any plausible existence, or that violate any of the necessary prerequisites for hydrocarbon accumulations. This is not to say that innovative rules cannot be incorporated; they can be added or subtracted at the will of the investigator.

An integrated GIS and knowledge-based system as an aid for the geological analysis of sedimentary basins

“…The inference engine contains the inference strategies and controls, explanations, and user-interface subsystems that experts employ when they manipulate facts and rules to reach a conclusion. Acquiring the knowledge for a KBS is often a bottleneck in the construction of expert systems ( Butler 1992 ), because it can involve formal, structured interviews, between the computer-oriented system developer and the domain specialist. In the work reported here the KBS designers were geologists with access to the sub-disciplinary experts in the host project team at the British Geological Survey.…”