Improved petrophysical characterization of Miocene deposits in south Tulamura anticline, India: An integrated geophysical and machine learning approach

Abstract

With the high demand for fossil fuels, exploring the frontier areas for hydrocarbon reserves has become imperative. The recent discoveries in Gojalia, Sonamura, Baramura, and Sundalbari fields emphasize the need to explore additional anticlinal structures in Tripura for hydrocarbon exploration. Tulamura anticline (the study area) produced gas from Upper Bhuban, establishing hydrocarbon prospectivity in the northern part, but the southern part remains largely unexplored. An electro-log interpretation revealed the presence of sand facies deposited in a fining upward sequence, suggesting channel deposition. An integrated geophysical approach using seismic inversion and machine learning techniques was performed to delineate and characterize the litho-facies dispersal patterns in the Tulamura field. Spectral decomposition (12, 20 and 28 Hz) of stacked seismic data were RGB (red-green-blue) blended, revealing the southward striking channel geometry of the Bhuban Formation at a depth of 2220 m. The 3D P-impedance and Vp/Vs ratio volumes were estimated using the model-based pre-stack seismic inversion. Inversion results help discriminate among sand, shale and siltstone litho-facies. Petrophysical property (effective porosity) was predicted by combining the post-stack seismic attributes and well-log data using neural network modelling. The identified sand facies within the channel geometry exhibit relatively moderate to low P-impedance (9800–10600 m/s * gm/cm³), low Vp/Vs ratio (1.68–1.76), and moderately high effective porosity (8–13%) from surroundings, indicating favourable conditions for hydrocarbon accumulations. Shale between channels and major faults can create favourable stratigraphic entrapment, while an upward fining sequence suggests an intact top seal. This study advocates an integrated approach involving geophysical inversion and machine learning to identify optimal conditions for hydrocarbon accumulation within sand facies, supported by structural and stratigraphic entrapment.