Abstract

Subsurface fluid flow involves migration of fluids from source to surface through a wide range of geologic structures, and thus plays a crucial role in unlocking potential hydrocarbon plays. Such studies have been mainly carried out in the marine environment by means of high-resolution seismic data in unravelling the fluid flow system and remains poorly documented for on-land data. This research attempts to explore the fluid flow activity in onshore petroliferous Indo-Gangetic peripheral foreland basin of India by using high-quality 3D seismic reflection data. Several seismic attributes have been utilised for efficiently describing the patterns of subsurface fluid flow structures. The attribute-responses of fluid flow are fused into a single attribute, called the Fluid Cube meta-attribute, by designing a workflow based on artificial neural network, which has enabled to elucidate the subsurface fluid migration routes. The subsurface fluid flow features are imaged as relatively vertical mounded structure with conical vent-like morphology. Internally, these features are associated with moderately distorted reflections. The reflections at the top are vertically stacked with medium to high amplitudes. The Fluid Cube meta-attribute demonstrates that subsurface fluid vertically migrates from the Neoproterozoic strata through minute fracture networks and weaker strata of the overlying Tertiary succession. The analysis of surficial geochemical anomalies corroborates quite reasonably with these observations. Thus, the Ganga peripheral foreland basin could be considered a promising area with potential leads that can be unlocked for hydrocarbon exploration. The analysis presented through this research could be efficiently carried out over other onshore basins worldwide.

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