Quantitative Prediction of Fracture Parameters in Volcanic Reservoirs Using Integrated Rock Physics and BO-BiLSTM Network Approach

Natural fractures significantly influence the production performance of volcanic hydrocarbon reservoirs. However, the complex nature of fracture networks affects the elastic behavior and seismic responses of these reservoirs, creating substantial challenges for seismic-based fracture characterization. This study proposes a method for quantitatively estimating fracture parameters in volcanic formations by integrating a rock physics model with the Bayesian optimized bidirectional long short-term memory (BO-BiLSTM) network architecture. Following the established model for volcanic formations, a model-based inversion approach is introduced to calculate fracture parameters using well log data, incorporating the simulated annealing particle swarm optimization (SA-PSO) algorithm for reliable multi-parameter simultaneous estimation. Fracture density is derived from these parameters, enabling a comprehensive assessment of fracture parameters. The validity of the volcanic model and inversion framework is confirmed by the strong correlation between predicted and observed velocities during the inversion process. Subsequently, the BO-BiLSTM architecture is used to build a predictive framework that captures the intricate relationships between fracture parameters and elastic properties. This predictive model is then applied to estimate fracture parameters from seismically-derived elastic properties. The seismic-based predictions exhibit strong agreement with results obtained from well log data, demonstrating the applicability of the proposed method. The predicted fracture parameters reflect micro-scale fracture characteristics and exhibit a structure-related distribution when visualized alongside macro-scale fractures, revealing patterns of fracture networks and potential connectivity between larger fractures. These insights enhance the understanding of multi-scale fracture systems, providing critical information for volcanic reservoir characterization.

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Publication Details

Journal
IEEE Transactions on Geoscience and Remote Sensing
Published
2025-01-01
DOI
https://doi.org/10.1109/tgrs.2025.3581090
Citations
2
Primary Topic
Seismic Imaging and Inversion Techniques
Type
article
Field-Weighted Citation Impact
3.73

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article

Quantitative Prediction of Fracture Parameters in Volcanic Reservoirs Using Integrated Rock Physics and BO-BiLSTM Network Approach

Zhiqi Guo, Yuedong Li, Cai Liu
2 citations
IEEE Transactions on Geoscience and Remote Sensing
Seismic Imaging and Inversion Techniques
3.73
article

Quantitative Prediction of Fracture Parameters in Volcanic Reservoirs Using Integrated Rock Physics and BO-BiLSTM Network Approach

Zhiqi Guo, Yuedong Li, Cai Liu
article en
2 citations

Abstract

Natural fractures significantly influence the production performance of volcanic hydrocarbon reservoirs. However, the complex nature of fracture networks affects the elastic behavior and seismic responses of these reservoirs, creating substantial challenges for seismic-based fracture characterization. This study proposes a method for quantitatively estimating fracture parameters in volcanic formations by integrating a rock physics model with the Bayesian optimized bidirectional long short-term memory (BO-BiLSTM) network architecture. Following the established model for volcanic formations, a model-based inversion approach is introduced to calculate fracture parameters using well log data, incorporating the simulated annealing particle swarm optimization (SA-PSO) algorithm for reliable multi-parameter simultaneous estimation. Fracture density is derived from these parameters, enabling a comprehensive assessment of fracture parameters. The validity of the volcanic model and inversion framework is confirmed by the strong correlation between predicted and observed velocities during the inversion process. Subsequently, the BO-BiLSTM architecture is used to build a predictive framework that captures the intricate relationships between fracture parameters and elastic properties. This predictive model is then applied to estimate fracture parameters from seismically-derived elastic properties. The seismic-based predictions exhibit strong agreement with results obtained from well log data, demonstrating the applicability of the proposed method. The predicted fracture parameters reflect micro-scale fracture characteristics and exhibit a structure-related distribution when visualized alongside macro-scale fractures, revealing patterns of fracture networks and potential connectivity between larger fractures. These insights enhance the understanding of multi-scale fracture systems, providing critical information for volcanic reservoir characterization.

IEEE Transactions on Geoscience and Remote SensingVol. 63
Jilin University (CN), Jilin Province Science and Technology Department (CN)
National Natural Science Foundation of China
Climate action
Openalex Percentile: Top 8%
Seismic Imaging and Inversion Techniques
3.73
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