Open AccessApplication of artificial intelligence to reservoir characterization: An interdisciplinary approach. Quarterly progress report, April 1 1996--June 30, 1996
Author(s) -
D. Kerr,
L.G. Thompson,
S. Shenoi
Publication year - 1997
Language(s) - English
Resource type - Reports
DOI - 10.2172/449843
Subject(s) - reservoir modeling , computer science , reservoir engineering , exploit , task (project management) , artificial intelligence , expert system , data science , data mining , systems engineering , geology , engineering , petroleum , petroleum engineering , paleontology , computer security
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 {open_quotes}soft{close_quotes} and {open_quotes}hard{close_quotes} data that ranges from qualitative and semi-quantitative geological interpretations to numeric data obtained from cores, well tests, well logs and production statistics. It involves significant amount 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