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Faults and fractures play a significant role in oilfield drilling operations, hydrocarbon trapping, and reservoir development. Exploring faults quickly and accurately can help reach the target more efficiently. In this study, applicable seismic attributes were combined using a multilayer perceptron and an unsupervised vector quantizer and applied to a 3D seismic cube to identify discontinuities. First, high-probability faulted areas were picked manually on a seismic section as an input pattern for the MLP. Then, particular seismic attributes (dip-steering, similarity, coherency, curvature, instantaneous) were applied to the data. Consequently, the MLP and UVQ were used to determine the most contributed attributes. Using the MLP and UVQ, the most relevant attributes were integrated to find faults and fractures in the 3D seismic volume. In contrast to some fault-identifying methods and prior studies, this study used not just steered attributes but also compared supervised and unsupervised neural networks. Eventually, comparing the outputs of the MLP, faulted and non-faulted cubes, with the initial seismic section and the UVQ’s output revealed discrepancies. For a specific set of attributes, the MLP was obviously superior to the UVQ in terms of creating detailed outputs, analyzing time, and rendering more precise and trustworthy results.

Faults and fractures detection using a combination of seismic attributes by the MLP and UVQ artificial neural networks in an Iranian oilfield