Application of artificial intelligence to reservoir characterization: An interdisciplinary approach, progress report, January 1, 1997--March 31, 1997

The basis of this research is to apply novel techniques from Artificial Intelligence and Expert Systems in capturing, integrating and articulating key knowledge from geology, geostatistics, and petroleum engineering to develop accurate descriptions of petroleum reservoirs. The ultimate goal is to design and implement a single powerful expert system for use by small producers and independents to efficiently exploit reservoirs. The main challenge of the proposed research is to automate the generation of detailed reservoir descriptions honoring all the available `soft` and `hard` data that ranges from qualitative and semi-quantitative geological interpretations to numeric data obtained from cores, well tests, well logs and production statistics. In this sense, the proposed research project is multidisciplinary. It involves significant amounts of information exchange between researchers in geology, geostatistics, and petroleum engineering. Computer science (and artificial intelligence) provides the means to effectively acquire, integrate and automate the key expertise in the various disciplines in a reservoir characterization expert system. Additional challenges are the verification and validation of the expert system, since much of the interpretation of the experts is based on extended experience in reservoir characterization. The overall project plan to design the system to create integrated reservoir descriptions begins by initially developing an Al-based methodology for producing large- scale reservoir descriptions generated interactively from geology and well test data. Parallel to this task is a second task that develops an Al-based methodology that uses facies-biased information to generate small-scale descriptions of reservoir properties such as permeability and porosity. The third task involves consolidation and integration of the large-scale and small-scale methodologies to produce reservoir descriptions honoring all the available data. The final task will be technology transfer. With this plan, we have carefully allocated and sequenced the activities involved in each of the tasks to promote concurrent progress towards the research objectives. Moreover, the project duties are divided among the faculty member participants. Graduate students will work in teams with faculty members. The results of the integration are not merely limited to obtaining better characterizations of individual reservoirs. They have the potential to significantly impact and advance the discipline of reservoir characterization itself