Exploring the utility of nonlinear hybrid optimization algorithms in seismic inversion: A comparative analysis

Kant, Ravi; Kumar, Brijesh; Maurya, S.P.; Singh, Raghav; Tiwari, Anoop Kumar

doi:10.1016/j.pce.2024.103754

Physics and Chemistry of the Earth, Parts A/B/C

2024

DOI: 10.1016/j.pce.2024.103754

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Exploring the utility of nonlinear hybrid optimization algorithms in seismic inversion: A comparative analysis

Ravi Kant,

Brijesh Kumar,

S.P. Maurya

et al.

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2024

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Cited by 3 publications

References 43 publications

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Seismic inversion based on principal component analysis and probabilistic neural network for prediction of porosity from post-stack seismic data

Verma,

kant,

Maurya

et al. 2024

Earth Sci Inform

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Seismic inversion based on principal component analysis and probabilistic neural network for prediction of porosity from post-stack seismic data

Verma,

kant,

Maurya

et al. 2024

Earth Sci Inform

View full text Add to dashboard Cite

A methodology for the mapping of acoustic impedance and porosity in the inter-well region using seismic inversion based on the Hooke and Jeeves algorithm

Kant,

Kumar,

Maurya

et al. 2024

Arab J Geosci

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Enhancing porosity prediction: Integrating seismic inversion utilizing sparse layer reflectivity, and particle swarm optimization with radial basis function neural networks

Kant,

Kumar,

Singh

et al. 2024

Geophysical Prospecting

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Seismic inversion, a crucial process in reservoir characterization, gains prominence in overcoming challenges associated with traditional methods, particularly in exploring deeper reservoirs. In this present study, we propose an inversion approach based on modern techniques like sparse layer reflectivity and particle swarm optimization to obtain inverted impedance. The proposed sparse layer reflectivity and particle swarm optimization techniques effectively minimize the error between recorded seismic reflection data and synthetic seismic data. This reduction in error facilitates accurate prediction of subsurface parameters, enabling comprehensive reservoir characterization. The inverted impedance obtained from both methods serves as a foundation for predicting porosity, utilizing a radial basis function neural network across the entire seismic volume. The study identifies a significant porosity zone (>20%) with a lower acoustic impedance of 6000–8500 m/s g cm3, interpreted as a sand channel or reservoir zone. This anomaly, between 1045 and 1065 ms two‐way travel time, provides high‐resolution insights into the subsurface. The particle swarm optimization algorithm shows higher correlation results, with 0.98 for impedance and 0.73 for porosity, compared to sparse layer reflectivity's 0.81 for impedance and 0.65 for porosity at well locations. Additionally, particle swarm optimization provides high‐resolution subsurface insights near well location and across a broader spatial range. This suggests particle swarm optimization's superior potential for delivering higher resolution outcomes compared to sparse layer reflectivity.

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Exploring the utility of nonlinear hybrid optimization algorithms in seismic inversion: A comparative analysis

Cited by 3 publications

References 43 publications

Seismic inversion based on principal component analysis and probabilistic neural network for prediction of porosity from post-stack seismic data

Seismic inversion based on principal component analysis and probabilistic neural network for prediction of porosity from post-stack seismic data

A methodology for the mapping of acoustic impedance and porosity in the inter-well region using seismic inversion based on the Hooke and Jeeves algorithm

Enhancing porosity prediction: Integrating seismic inversion utilizing sparse layer reflectivity, and particle swarm optimization with radial basis function neural networks

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