Lithofacies logging identification for strongly heterogeneous deep-buried reservoirs based on improved Bayesian inversion: The Lower Jurassic sandstone, Central Junggar Basin, China - Zheng, Zongyuan; Zhang, Likuan; Cheng, Ming; Lei, Yuhong; Zhang, Zengbao; Zeng, Zhiping; Ren, Xincheng; Yu, Lan; Yang, Wenxiu; Li, Chao; Liu, Naigui (Frontiers in Earth Science, 2023)
The strong heterogeneity characteristics of deep-buried clastic low-permeability reservoirs may lead to great risks in hydrocarbon exploration and development, which makes the accurate identification and prediction of reservoir lithofacies crucial for improving the obtained exploration results. Due to the very limited core data available from deep drilling, lithofacies logging identification is the most important method for comprehensively obtaining the rock information of deep-buried reservoirs and is an important basic task for carrying out reservoir characterization and geological modeling. In this paper, a machine learning method is introduced to lithofacies logging identification to explore an accurate lithofacies identification method under frequent reservoir petrography changes, with the deep fluvial-delta sandstone reservoir of the Sangonghe Formation in the Central Junggar Basin taken as an example. The K-means-based synthetic minority oversampling technique (K-means SMOTE) is employed to solve the problem of imbalanced lithofacies data categories used to calibrate logging data, and a probabilistic calibration method is introduced to correct the likelihood function so that different categories can be adequately learned. To address the situation in which traditional machine learning methods ignore the geological deposition process, we introduce a depositional prior for controlling the vertical spreading process based on a Markov chain and propose an improved Bayesian inversion process for training on the log data to identify lithofacies, which significantly improves the accuracy and recall rate of the method. The practical applications and comparison results show that the proposed method can effectively identify the lithofacies distributions of deep, strongly heterogeneous sandstone reservoirs.