3D model of water saturation, effective porosity and volume of shale in upper Assam Shelf, India using multi-attribute regression and cascade-probabilistic neural network

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Quantitative interpretation of geophysical data for reservoir identification to study the vertical and horizontal variation of lithofacies, involves integration of multiple disparate datasets. We use a 3D seismic data and two wells (Km and Te) to predict a petrophysical model within the Paleocene to Oligocene formations, and the Precambrian basement in the upper Assam Shelf. The conventional well data were examined in each well to identify reservoirs within the formations. This is followed by a model based seismic inversion of 3D post-stack seismic data resulting in a 3D impedance model. Next, the cascade-probabilistic neural network (CPNN) was trained using the well log derived attributes (target label data) at the collocated seismic observations (observed features). We were able to successfully train the network to predict water saturation, effective porosity, and volume of shale from the inverted seismic traces with the cross-correlation of range 0.96, 0.97, 0.97, respectively. The optimum set of multi-attributes at well-seismic tie locations are extracted from the seismic data and the selected optimum set of attributes are used at all locations in the application of CPNN. CPNN is found to train the target data better than probabilistic neural network and multi-layer feedforward neural network. Hydrocarbon reservoirs are found in shaly sandstones within the Kopili and Sylhet formations of Eocene and Paleocene ages, and Precambrian basement. The hydrocarbon reservoir shows the variation in water saturation 20% to 50%, with the volume of shale of range 40% to 60%, and the effective porosity of range 8% to 30% respectively.