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Integrated thin layer classification and reservoir characterization using sparse layer reflectivity inversion and radial basis function neural network: a case study

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Abstract

Understanding subterranean reservoirs, geological characteristics, fluid composition, and hydrocarbon potential strongly relies on precise reservoir characterization. Seismic inversion is a key method in reservoir characterization to approximate the acoustic impedance and porosity of underlying rock formations using seismic and well-log data. A sparse layer reflectivity (SLR) post-stack inversion method approach is used in this study to make thin layers more visible. To generate an impedance volume, it uses a predetermined wavelet library, an objective function, and a regularization parameter, the regularization parameter is a tunable parameter used to control the balance between fitting the data closely (minimizing the misfit) and ensuring a smooth and stable model for and sparseness computed coefficients. This study uses Blackfoot data to estimate the density, velocity, impedance, and porosity of a particular region using the SLR and Radial Basis Function Neural Network (RBFNN). According to the interpretation of the impedance section, a low impedance anomaly zone with an impedance range of (8500–9000)  m/s*g/cc is present at a time of (1040–1065) ms. The low impedance zone is classified as a clastic glauconitic sand channel (reservoir zone) based on the correlation between seismic and borehole data. Further, a Radial Basis Function Neural Network (RBFNN) has been applied to the data to estimate porosity volume and to conduct a more thorough examination of the reservoir zone and cross-validate inverted results. The research demonstrates that the high porosity zone, low velocity, and density zone are discovered by the RBFNN technique, and the low impedance zone interpreted in inversion findings are correlating, which confirms the existence of the glauconitic sand channel. This research is crucial for understanding how well SLR, RBFNN, and multi-attribute analysis work to define sand channels.

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Acknowledgements

The authors acknowledge the CGG Geo software for providing Hampson Russell software and data. One of the authors (S.P. Maurya) gratefully acknowledges the funding agency, UGC-BSR (M-14-0585) and IoE BHU (Dev. Scheme no. 6031B) for their financial support. Apart from that, we also thank www.mathworks.com and www.norsar.no for providing Matlab (2022b) and Norsar (complete package) academic licenses, respectively. Without their support, this work would not be possible.

Funding

This study was supported by the agency, UGC-BSR, Govt. of India (M-14-0585) and IoE BHU (Dev. Scheme No. 6031B).

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Authors and Affiliations

  1. Department of Geophysics, Banaras Hindu University, Varanasi, U.P., 221005, India

    Raghav Singh, Aditya Srivastava, Ravi Kant, S. P. Maurya, Nitin Verma, G. Hema, P. K. Kushwaha,  Richa, Ajay P. Singh & M. K. Srivastava

  2. Department of Earth Sciences, Indian Institute of Technology Bombay, Mumbai, 400076, India

    K. H. Singh

  3. Telesto Energy India Pvt. Ltd., Coimbatore, Tamil Nadu, 641062, India

    P. Mahadasu

  4. Rajiv Gandhi Institute of Petroleum Technology, Jais, Amethi, 229305, India

    Piyush Sarkar

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  1. Raghav Singh
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  8. P. K. Kushwaha
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  9. Richa
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Contributions

RS and AS wrote the manuscript, RK performed analysis and all authors reviewed the manuscript.

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Correspondence to Ravi Kant.

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Singh, R., Srivastava, A., Kant, R. et al. Integrated thin layer classification and reservoir characterization using sparse layer reflectivity inversion and radial basis function neural network: a case study. Mar Geophys Res 45, 3 (2024). https://doi.org/10.1007/s11001-023-09537-w

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