Quantitative Seismic Interpretation of Reservoir Parameters and Elastic Anisotropy Based on Rock Physics Model and Neural Network Framework in the Shale Oil Reservoir of the Qianjiang Formation, Jianghan Basin, China

Zhiqi Guo, Tao Zhang, Cai Liu, Xiwu Liu and Yuwei Liu
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Zhiqi Guo: College of Geoexploration Science and Technology, Jilin University, Changchun 130021, China
Tao Zhang: SINOPEC Geophysical Research Institute, Nanjing 211100, China
Cai Liu: College of Geoexploration Science and Technology, Jilin University, Changchun 130021, China
Xiwu Liu: State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development, Beijing 100083, China
Yuwei Liu: State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development, Beijing 100083, China

Energies, 2022, vol. 15, issue 15, 1-16

Abstract: Quantitative estimates of reservoir parameters and elastic anisotropy using seismic methods is essential for characterizing shale oil reservoirs. Rock physics models were established to quantify elastic anisotropy associated with clay properties, laminated microstructures, and bedding fractures at different scales in shale. The inversion schemes based on the built rock physics models were proposed to estimate reservoir parameters and elastic anisotropy using well log data. Based on the back propagation neural network framework, the obtained rock physical inversion results were used to establish the nonlinear models between elastic properties and reservoir parameters and elastic anisotropy of shale. The established correlations were applied for quantitative seismic interpretation, converting seismic inversion results to the reservoir parameters and elastic anisotropy to characterize the shale oil reservoir comprehensively. The predicted elastic anisotropy of the shale matrix reflects the lamination degree and the mechanical properties of the shale, which is critical for the effective implementation of hydraulic fracturing. The calculated elastic anisotropy of the shale provides more accurate models for seismic modeling and inversion. The obtained bedding fracture parameters provide insights into reservoir permeability. Therefore, the proposed method provides valuable information for identifying favorable oil zones in the study area.

Keywords: shale oil; rock physics model; reservoir parameters; elastic anisotropy; quantitative seismic interpretation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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