Our research field is in hydrocarbon exploration, development, and production. We use various well log data (conventional to advanced), seismic data (2D and 3D), and lab testing measurements. We estimate the number of critical parameters for reservoir estimations, reservoir characterization, and field development, such as wellbore stability, drilling hazards, and stress modelling, which are as follows below:
Lab measurement:
Reservoir Geophysics lab: lab measurement of P- and S-wave velocity, Poisson's ratio with other rock modulus properties, and Porosity & Permeability of reservoir and non-reservoir rock
Computational, analysis and modelling:
a) Petrophysics and Rockphysics: Saturation estimation, Lithofacies modelling, porosity and permeability modelling, relationship of P-wave and S-wave velocity with other properties
b) Reservoir Geomechanics: Pore pressure modelling and over pressure zones identification, In-situ stress magnitude and stress orientation, and rock modulus properties., and reservoir couple fluid modelling
c) Seismic attributes and AVO analysis for fluid identifaction and quantification.
d) Application pf Machine Learning techniques, Deep neural network techniques and other simulations methods for solving complex problem of the reservoir rocks.

We apply all the above mentioned techniques in the conventional oil/gas reservoirs and as well as in the Gas hydrates reservoir, Coal & CBM exploration & production, Shale gas reservoir, Geothermal and CO2 sequestrations

All Publications

A data-driven approach for identification of coal-related lithofacies using single and meta-learner ensemble classifiers from well-log data: A case study from Sohagpur coal field, India by Roy R., Singha D. K., Ghosh S. , Abbas L. , Mondal D. Journal of Applied Geophysics 246 - (2026)
A Comprehensive Study on Fracture Identification and Fractures Properties Estimation from Well Log Data in the Hydrocarbon Bearing Formation Using Semi-automated Machine Learning and Dual Porosity Model by Singha D.K., Sarkar A. Pure and Applied Geophysics 182 3613-3637 (2025)
Identification of lithofacies from well log data in the upper Assam basin using machine learning techniques by Das S., Singha D. K., Mandal P. P., Agrahari S. Acta Geophysica - (2024)
Rock Physics Modelling for Estimation of Gas Hydrate Saturation Using NGHP-02 Well Data in the Krishna Godavari Basin by Yadav P. K., Singha D. K., Sain K. Pure and Applied Geophysics 180 2999-3018 (2023)
Pore pressure imaging in the over-pressurized facies using the ANN techniques through seismic inversion and well-log approach by Singha D. K., Abbas L. 2025 International Meeting for Applied Geoscience & Energy (IMAGE) - (2025)
A Machine Learning framework for lithofacies identification: A case study from HPHT KG basin by Abbas L., Singha D. K. 15th Biennial International conference and Exposition - (2025)
Reservoir geomechanics and petrophysics using NGHP-02 logging data: Implications for depressurization of the gas-hydrate layers by Singha D. K., Roy R. , Sain K. 15th Biennial International conference and Exposition - (2025)
Sensitivity analysis of boundary conditions on FEM based based static structural geomechanical model of gas hydrate reservoirs at NGHP-02 sites by Roy R., Singha D. K., Sain K. 15th Biennial International conference and Exposition - (2025)

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