Abstract
This study demonstrates an integrated approach using a seismic-coloured inversion to derive the hydrocarbon potential of a field in the Upper Assam basin. The well-log analysis indicated the presence of hydrocarbon in the form of gas, oil, and water in sandstones with thin streaks of limestone and shale layers. The Acoustic Impedance (AI) model was generated from post-stack seismic data using a coloured inversion. Shale volume (Vshale), density, porosity and water saturation are the petrophysical parameters spatially populated using a multilayered feed-forward neural network on the inversion-based AI model. The estimated range of model-derived parameters varies as Velocity: 2136–5034 m/s, Vshale: 0–48%, Density: 2.14–2.72 gm/cc, Porosity: 6–29%, Water saturation: 9–28%, and demonstrates a reasonable correlation to well-log data. Seismic attribute for thin-fault likelihood was used to interpret the major faults. From the combined analysis based on interpreted faults and modelled parameters, the northwestern part of the study area displays a thick hydrocarbon-bearing zone (due to increasing thickness and reservoir quality). This interpretation is based on the consideration that faults are open. Thus, assuming continuity in the sequence, this northwestern region can be considered as one of the promising candidates for further exploration.
Published Version
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