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Qualitative and quantitative reservoir characterisation using seismic inversion based on global optimization: A comparative case study

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Abstract

In this study, the focus is on predicting the properties of rocks beneath the Earth’s surface using global optimisation techniques such as genetic algorithms (GA), simulated annealing (SA) and particle swarm optimisation (PSO). The goal is to minimise the difference (error) between actual seismic data and synthetic (computed) seismic traces. Global optimisation is an approach that is independent of the initial model and aims to identify the global minimum of an objective function. In contrast, local optimisation relies on the accuracy of the initial model, and if an accurate initial model is not provided, it may become trapped in a local minimum, leading to an inaccurate representation of the subsurface model. What makes global optimisation powerful is that it does not get stuck in local minima (suboptimal solutions), but seeks the absolute best solution in the entire search space. This property is crucial in seismic inversion, where finding the most accurate representation of subsurface properties is of utmost importance for geophysical applications. The study includes one synthetic example and one real dataset, with a specific emphasis on evaluating acoustic impedance rock properties. While acoustic impedance is characteristic of rock layers, seismic data represents properties at the interfaces between these layers. Consequently, seismic data is highly valuable for gaining detailed insights into the subsurface. The results of the optimisation process provide exceptionally detailed views of the subsurface, aiding in the interpretation of seismic data. GA, SA and PSO algorithms perform well, both with synthetic data and real data. The inversion process identifies a zone with low acoustic impedance, corresponding to a prominent seismic anomaly. The evaluation of the inverted outcomes reveals that the impedance within the area ranges from 4300 to 4700 m/s*g/cc, situated within a specific time range of 900–950 ms in the seismic data of F3-block, Netherland.

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Acknowledgements

We thank GeoSoftware for supplying Hampson Russell software, including the emerge and stratum modules, to Banaras Hindu University. In addition, one of the authors (Dr S P Maurya) thank the funding organisations UGC-BSR (M-14-0585) and IoE BHU (Dev. Scheme no. 6031B and I-6031D (RJP-PDF, Scheme No. 3254)) for their financial help. Furthermore, we recognise the academic licences for Matlab (2022b) and Norsar (complete package), which may be obtained from www.mathworks.com and www.norsar.no, respectively. This task would be impossible to complete without their assistance.

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Authors and Affiliations

  1. Department of Mining Engineering, Indian Institute of Technology, Banaras Hindu University, Varanasi, 221 005, India

    Brijesh Kumar

  2. Department of Geophysics, Institute of Science, Banaras Hindu University, Varanasi, 221 005, India

    Brijesh Kumar, Ravi Kant & S P Maurya

Authors
  1. Brijesh Kumar
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  2. Ravi Kant
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  3. S P Maurya
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Contributions

All authors contributed to this manuscript. Brijesh Kumar: Collected data, designed and performed analysis, and wrote the first draft of the manuscript. Ravi Kant: Collected data, prepared maps and figures, and provided inputs in writing and editing the manuscript. S P Maurya: Conceived and designed the analysis; contributed to analysis tools and methodology; provided inputs in writing the manuscript and editing.

Corresponding author

Correspondence to Brijesh Kumar.

Additional information

Communicated by Bappa Mukherjee

This article is part of the Topical Collection: AI/ML in Earth System Sciences.

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Kumar, B., Kant, R. & Maurya, S.P. Qualitative and quantitative reservoir characterisation using seismic inversion based on global optimization: A comparative case study. J Earth Syst Sci 133, 87 (2024). https://doi.org/10.1007/s12040-024-02301-7

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  • DOI: https://doi.org/10.1007/s12040-024-02301-7

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