Reservoir characterization using simultaneous inversion of pre-stack seismic data based on traditional conjugate gradient methods and particle swarm optimization: A comparative case study
Shranjeno v:
| izdano v: | Earth Science Informatics vol. 18, no. 1 (Jan 2025), p. 137 |
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| Izdano: |
Springer Nature B.V.
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| Teme: | |
| Online dostop: | Citation/Abstract Full Text - PDF |
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| 245 | 1 | |a Reservoir characterization using simultaneous inversion of pre-stack seismic data based on traditional conjugate gradient methods and particle swarm optimization: A comparative case study | |
| 260 | |b Springer Nature B.V. |c Jan 2025 | ||
| 513 | |a Case Study Journal Article | ||
| 520 | 3 | |a Seismic inversion is a geophysical method that converts seismic reflection data into a quantitative representation of a reservoir's geological properties. These parameters are crucial for predicting reservoir rocks and the fluids present in the subsurface. Seismic inversion has been categorized into two ways: post-stack and pre-stack inversion. Pre-stack seismic inversion provides more detailed properties of the subsurface as compared to post-stack inversion. This study focuses on pre-stack seismic inversion using the traditional conjugate gradient methods and a novel methodology based on particle swarm optimization (PSO) techniques. Pre-stack inversion inherently utilizes amplitude variation with offset (AVO), which provides critical information about the elastic properties of the subsurface. The conjugate gradient method is a local optimization technique that can converge at local optima, potentially leading to false solutions to the inverse problem. To overcome these drawbacks, PSO, a global optimization technique with a tendency to converge at global optima, was employed. These methods were employed in the Penobscot field in Canada in two phases. Initially, the composite trace was inverted and then compared to the original well-log data. The full seismic volume was then inverted to calculate P-velocity, S-velocity, and density. The inverted results from both methods provided high-resolution subsurface information, but the PSO-based seismic inversion showed significantly better results compared to traditional methods. The conjugate gradient method attained a correlation of 0.89 with a RMS error of 0.33, while the PSO-based inversion attained a correlation of 0.95 with RMS error of 0.23. Additional statistical parameters also demonstrated that the PSO-based seismic inversion offered more detailed and higher-resolution subsurface information compared to the traditional pre-stack seismic inversion utilizing conjugate gradient methods. | |
| 653 | |a Particle swarm optimization | ||
| 653 | |a Elastic properties | ||
| 653 | |a Inverse problems | ||
| 653 | |a Optimization techniques | ||
| 653 | |a Global optimization | ||
| 653 | |a Comparative studies | ||
| 653 | |a Seismological data | ||
| 653 | |a Conjugate gradient method | ||
| 653 | |a Reservoirs | ||
| 653 | |a Local optimization | ||
| 653 | |a Geophysical methods | ||
| 653 | |a Parameters | ||
| 653 | |a Seismic surveys | ||
| 653 | |a Seismic activity | ||
| 653 | |a Environmental | ||
| 773 | 0 | |t Earth Science Informatics |g vol. 18, no. 1 (Jan 2025), p. 137 | |
| 786 | 0 | |d ProQuest |t Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3151010417/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3151010417/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |