Deep learning for predicting porosity in ultra-deep fractured vuggy reservoirs from the Shunbei oilfield in Tarim Basin, China

Deep and ultra-deep carbonate reservoirs in China, which account for 34% of the country’s oil and gas reserves, pose significant challenges for porosity prediction due to their complex geological features, including extensive burial depth, weak seismic signals, and high heterogeneity. To address the...

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Bibliographic Details
Published in:Scientific reports 2024-11, Vol.14 (1), p.29605-14
Main Authors: Deng, Ziyan, Zhou, Dongsheng, Dong, Hezheng, Huang, Xiaowei, Wei, Shiping, Kang, Zhijiang
Format: Article
Language:English
Subjects:
704/2151
704/2151/213
704/2151/2809
704/2151/508
Deep learning
Fractured-vuggy reservoirs
Heterogeneity
Humanities and Social Sciences
Karst
multidisciplinary
Oil and gas fields
Porosity
Prediction models
Principal components analysis
Reservoirs
Science
Science (multidisciplinary)
Shunbei
Tarim basin
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Summary:Deep and ultra-deep carbonate reservoirs in China, which account for 34% of the country’s oil and gas reserves, pose significant challenges for porosity prediction due to their complex geological features, including extensive burial depth, weak seismic signals, and high heterogeneity. To address these challenges, this study develops an advanced deep learning approach specifically designed for ultra-deep, fault-controlled, fractured-vuggy reservoirs in the Tarim Basin. The study utilizes a three-dimensional seismic dataset and applies Principal Component Analysis (PCA) to select five key features from eight seismic attributes. Additionally, seismic phase-controlled constraints are incorporated into the model. Using deep learning technology, a porosity prediction model for ultra-deep carbonate reservoirs has been constructed. Validation using blind wells from the Shunbei oilfield shows that this approach achieves a 76% reduction in Mean Square Error (MSE) compared to traditional impedance inversion techniques, highlighting its high predictive accuracy. Through SHapley Additive exPlanations (SHAP) analysis, the attributes LAMBDA_AAGFIL and PHASE_ANT are identified as the most influential, highlighting their importance in representing karst cave and fracture structures within the reservoir. These findings underscore the innovation and substantial improvement of the proposed method over conventional techniques, offering a robust and high-precision approach for porosity prediction in ultra-deep carbonate reservoirs.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-81051-4