Stress Barrier Characterization and Application for Engineering Sweet Spot Prediction in Deep Shale Gas Reservoirs

Abstract The presence of stress barriers significantly influences the extension of hydraulic fracturing networks and the volume of reservoir stimulation. In the southern Sichuan Basin, deep shale gas reservoirs, particularly those in the Longmaxi formation, exhibit strong anisotropy and heterogeneity. These reservoirs are characterized by high pore pressure, multi-level faults and fractures, and complex in-situ stress conditions, which collectively pose challenges in accurately characterizing stress barriers. This study aims to develop a high-resolution, three-dimensional geomechanical model to precisely characterize the stress barriers within the Longmaxi deep shale gas reservoirs. By integrating various geological and geophysical data, including seismic attributes and single-well acoustic logging data, the model will provide a detailed representation of the stress distribution and its impact on hydraulic fracturing. The accurate characterization of stress barriers is crucial for predicting engineering sweet spots within the reservoir that are most favorable for hydraulic fracturing. By identifying these sweet spots, the model can guide the optimization of hydraulic fracturing operations, ultimately improving the efficiency and effectiveness of reservoir stimulation. This approach not only enhances our understanding of the subsurface stress field but also contributes to more efficient and sustainable exploitation of deep shale gas resources in the southern Sichuan Basin.

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Publication Details

Published
2025-06-02
DOI
https://doi.org/10.2118/225288-ms
Citations
1
Primary Topic
Seismic Imaging and Inversion Techniques
Type
article
Field-Weighted Citation Impact
1.87
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article

Stress Barrier Characterization and Application for Engineering Sweet Spot Prediction in Deep Shale Gas Reservoirs

Xiaoxu Ren, Yanming Tong, Ersi Xu, Yuanwei Pan et al.
1 citations
Seismic Imaging and Inversion Techniques
1.87
article

Stress Barrier Characterization and Application for Engineering Sweet Spot Prediction in Deep Shale Gas Reservoirs

Xiaoxu Ren, Yanming Tong, Ersi Xu, Yuanwei Pan, R. Zhang, Baoqing Zeng, Peng Liu, Ting Yu, Haoyong Huang, Li Peng Wang, Xing Yang
article en
1 citations

Abstract

Abstract The presence of stress barriers significantly influences the extension of hydraulic fracturing networks and the volume of reservoir stimulation. In the southern Sichuan Basin, deep shale gas reservoirs, particularly those in the Longmaxi formation, exhibit strong anisotropy and heterogeneity. These reservoirs are characterized by high pore pressure, multi-level faults and fractures, and complex in-situ stress conditions, which collectively pose challenges in accurately characterizing stress barriers. This study aims to develop a high-resolution, three-dimensional geomechanical model to precisely characterize the stress barriers within the Longmaxi deep shale gas reservoirs. By integrating various geological and geophysical data, including seismic attributes and single-well acoustic logging data, the model will provide a detailed representation of the stress distribution and its impact on hydraulic fracturing. The accurate characterization of stress barriers is crucial for predicting engineering sweet spots within the reservoir that are most favorable for hydraulic fracturing. By identifying these sweet spots, the model can guide the optimization of hydraulic fracturing operations, ultimately improving the efficiency and effectiveness of reservoir stimulation. This approach not only enhances our understanding of the subsurface stress field but also contributes to more efficient and sustainable exploitation of deep shale gas resources in the southern Sichuan Basin.

PetroChina Southwest Oil and Gas Field Company (China)
Openalex Percentile: Top 18%
Seismic Imaging and Inversion Techniques
1.87
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Stress Barrier Characterization and Application for Engineering Sweet Spot Prediction in Deep Shale Gas Reservoirs — Xiaoxu Ren, Yanming Tong, et al. · (2025) | TGRS Research Map | TGRS