Risk Reduction With a Fuzzy Expert Exploration Tool (open access)

Risk Reduction With a Fuzzy Expert Exploration Tool

Incomplete or sparse information on types of data such as geologic or formation characteristics introduces a high level of risk for oil exploration and development projects. ''Expert'' systems developed and used in several disciplines and industries, including medical diagnostics, have demonstrated beneficial results. A state-of-the-art exploration ''expert'' tool, relying on a computerized data base and computer maps generated by neural networks, is proposed for development through the use of ''fuzzy'' logic, a relatively new mathematical treatment of imprecise or non-explicit parameters and values. Oil prospecting risk can be reduced with the use of a properly developed and validated ''Fuzzy Expert Exploration (FEE) Tool.'' This tool will be beneficial in many regions of the US, enabling risk reduction in oil and gas prospecting and decreased prospecting and development costs. In the 1998-1999 oil industry environment, many smaller exploration companies lacked the resources of a pool of expert exploration personnel. Downsizing, low oil prices and scarcity of exploration funds have also affected larger companies, and will, with time, affect the end users of oil industry products in the US as reserves are depleted. The proposed expert exploration tool will benefit a diverse group in the US, leading to a more efficient use …
Date: December 31, 2000
Creator: Weiss, William W.
System: The UNT Digital Library
Risk Reduction With a Fuzzy Expert Exploration Tool (open access)

Risk Reduction With a Fuzzy Expert Exploration Tool

Incomplete or sparse information on geologic or formation characteristics introduces a high level of risk for oil exploration and development projects. Expert systems have been developed and used in several disciplines and industries, including medical diagnostics, with favorable results. A state-of-the-art exploration ''expert'' tool, relying on a computerized data base and computer maps generated by neural networks, is proposed through the use of ''fuzzy'' logic, a relatively new mathematical treatment of imprecise or non-explicit parameters and values. This project will develop an Artificial Intelligence system that will draw upon a wide variety of information to provide realistic estimates of risk. ''Fuzzy logic,'' a system of integrating large amounts of inexact, incomplete information with modern computational methods to derive usable conclusions, has been demonstrated as a cost-effective computational technology in many industrial applications. During project year 1, 90% of geologic, geophysical, production and price data were assimilated for installation into the database. Logs provided geologic data consisting of formation tops of the Brushy Canyon, Lower Brushy Canyon, and Bone Springs zones of 700 wells used to construct regional cross sections. Regional structure and isopach maps were constructed using kriging to interpolate between the measured points. One of the structure derivative maps …
Date: June 30, 2000
Creator: Weiss, William W.
System: The UNT Digital Library