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 of scarce funds and lower product prices for consumers.

Summary of Progress

During this six-month period the majority of data acquisition for this project 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 wire line logs as inputs, providing another tool for estimating both the potential success of a well, and the interval to perforate.

Regional attributes have been gridded to a 40-ac bin (gridblock) size and our fuzzy ranking procedures have been applied to determine which attributes are best able to predict production trends in the basin, using the average value of the first 12 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 published.1 Significant improvements over standard techniques were found particularly when test wells were on the dataset boundary where extrapolation is required.

An initial step in 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 will be operated on in parallel to increase efficiency of the overall system.

Coding of the Java interface, which users will utilize to access data in the online databases and run the expert system, has begun. Development of the interface will be an important ongoing project over the next year and will eventually tie together the data and the expert system programs coded in JESS while allowing user customization and informative reports of results to be returned.

This content is only available via PDF.