Mining and fusion of petroleum data with fuzzy logic and neural network agents

Abstract Analyzing data from well logs and seismic is often a complex and laborious process because a physical relationship cannot be established to show how the data are correlated. In this study, we will develop the next generation of “intelligent” software that will identify the nonlinear relationship and mapping between well logs/rock properties and seismic information and extract rock properties, relevant reservoir information and rules (knowledge) from these databases. The software will use fuzzy logic techniques because the data and our requirements are imperfect. In addition, it will use neural network techniques, since the functional structure of the data is unknown. In particular, the software will be used to group data into important data sets; extract and classify dominant and interesting patterns that exist between these data sets; discover secondary, tertiary and higher-order data patterns; and discover expected and unexpected structural relationships between data sets.

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