Detection of hydrocarbon reservoir boundaries using neural network analysis of surface geochemical data

Abstract

Surface geochemical surveys could become important tools for defining the boundaries of a hydrocarbon reservoir. Conventional statistical analysis has shown that a correlation can indeed be found between surface geochemical data and the location of a sample site with respect to the boundaries of a known reservoir. However, such analysis methods cannot be used directly as predictive tools. This article describes the successful application of artificial intelligence in the form of neural network analysis to determine whether a specific sample site, given the ethane concentration in the soil and certain environmental data, is within the surface trace of the reservoir boundaries. Data from a previous study over a known gas storage reservoir were used to train a back-propagation neural network. No attempt was made to optimize the structure of the network. We used 85% of the data to train the network and withheld 15% to act as unknowns. The input variables consisted of adsorbed ethane concentration and a series of soil description and environmental parameters. The output variable was a simple binary reflecting whether the sample site was directly over the reservoir. The final network was able to predict 95% of unknown sample sites. We found it necessary to include in the input data the ethane concentrations for sites on either side of each site studied. This is consistent with previous observations that a series of adjacent sites having anomalous concentrations hold more significance than do isolated sites. We also found that the use of the land (probably reflecting the degree of disturbance) and soil moisture are the most important environmental variables. This is consistent with previous conventional statistical studies of the same data. We conclude that application of neural networks to properly designed surface geochemical studies holds promise for use in defining the boundaries of known reservoirs. (Begin page 1894)