Abstract
An artificial intelligence technique of fuzzy associative memory is used to determine rock types from well-log signatures. Fuzzy associative memory (FAM) is a hybrid of neural network and fuzzy expert system. This new approach combines the learning ability of neural network and the strengths of fuzzy linguistic modeling to adaptively infer lithologies from well-log signatures based on (1) the relationships between the lithology and log signature that the neural network have learned during the training and/or (2) geologist's knowledge about the rocks. The method is applied to a sequence of the Ordovician rock units in northern Kansas. This paper also compares the performances of two different methods, using the same data set for meaningful comparison. The advantages of FAM are: (1) expert knowledge acquired by geologists is fully utilized; (2) this knowledge is augmented by the neural network learning from the data, when available; and (3) FAM is transparent in that the knowledge is explicitly stated in the fuzzy rules.
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