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 have demonstrated beneficial results. A state-of-the-art exploration ''expert'' tool, relying on a computerized database and computer maps generated by neural networks, is being developed 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 FEE Tool can be beneficial in many regions of the U.S. by enabling risk reduction in oil and gas prospecting as well as 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 U.S. as reserves are depleted. The FEE Tool will benefit a diverse group in the U.S., leading to a more efficient use of scarce funds and lower product prices for consumers. This second annual report contains a summary of progress to date, problems encountered, plans for the next quarter, and an assessment of the prospects for future progress. During the second year of the project, data acquisition of the Brushy Canyon Formation was completed with the compiling and analyzing of well logs, geophysical data, and production information needed to characterize production potential in the Delaware Basin. A majority of this data now resides in several online databases on our servers and is in proper form to be accessed by external programs such as Web applications. A new concept was developed and tested in well log analysis using neural networks. Bulk volume oil (BVO) was successfully predicted using wireline logs as inputs. This concept provides a new tool for estimating the potential success of a well and determining the productive interval to be perforated. Regional attributes have been gridded to a 40-ac bin (gridblock) size, and our fuzzy ranking procedures were applied to determine which attributes are best able to predict production trends in the Delaware Basin. The production indicator was the average of the first 12 full producing months of oil production as the value to be predicted. A study to determine the ability of an artificial intelligence system to predict depth using seismic attributes in a Delaware field was completed and the results were published. Significant improvements over standard techniques were found, particularly when test wells were on the dataset boundary where extrapolation is required. Programming the expert system was undertaken, and a decision tree program was coded in Java Expert System Shell (JESS) that allows development and tabulation of rules and relationships between rules that can be used by our expert system. This important program allows lists of rules to be entered and easily tested and verified. The design of the expert system itself was clarified and an expanded system was created where several distinct factors such as geologic/geophysical data, trap assessment, and formation assessment can be operated on in parallel to increase efficiency of the overall system. Coding of the Java interface, which users can use to access data in the online databases and run the expert system, was completed. Development of the interface ties together the data and the expert system programs coded in JESS while allowing user customization and informative reports of results to be retrieved. Technology transfer continued to be an important aspect of this project. Research and progress to date was presented to a group of industry and academic professionals at the second annual consortium meeting held November 2, 2000 in Hobbs, NM. Key technical results from the project were reported in nine papers and posters that were presented during the second year of the project.