Deng Ziyan, Zhou Dongsheng, Dong Hezheng, Huang Xiaowei, Wei Shiping, Kang Zhijiang
Key Laboratory of Polar Geology and Marine Mineral Resources (China University of Geosciences, Beijing), Ministry of Education, Beijing, 100083, China.
School of Ocean Sciences, China University of Geosciences (Beijing), Beijing, 100083, China.
Sci Rep. 2024 Nov 28;14(1):29605. doi: 10.1038/s41598-024-81051-4.
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.
中国的深层和超深层碳酸盐岩储层占全国油气储量的34%，由于其复杂的地质特征，包括埋藏深度大、地震信号弱和非均质性高，给孔隙度预测带来了重大挑战。为应对这些挑战，本研究开发了一种先进的深度学习方法，专门针对塔里木盆地超深层、断层控制的缝洞型储层。该研究利用三维地震数据集，并应用主成分分析（PCA）从八个地震属性中选择五个关键特征。此外，将地震相控约束纳入模型。利用深度学习技术，构建了超深层碳酸盐岩储层孔隙度预测模型。利用顺北油田的盲井进行验证表明，与传统的波阻抗反演技术相比，该方法的均方误差（MSE）降低了76%，突出了其高预测精度。通过SHapley加性解释（SHAP）分析，确定属性LAMBDA_AAGFIL和PHASE_ANT为最具影响力的属性，突出了它们在表征储层内溶洞和裂缝结构方面的重要性。这些发现强调了所提方法相对于传统技术的创新性和实质性改进，为超深层碳酸盐岩储层孔隙度预测提供了一种强大且高精度的方法。
