关键词: 筇竹寺组, 深层页岩气储层, 页岩岩相分类, 工作流, 神经网络聚类分析

Abstract: With the significant breakthrough in shale gas exploration of the Lower Cambrian Qiongzhusi Formation by SINOPEC Southwest Oil and Gas Company, the hot spot of marine shale gas exploration in Sichuan is gradually shifting from the Longmaxi Formation to the Qiongzhusi Formation. Therefore, how to accurately identify shale lithology is a difficult problem that needs to be solved in current exploration work. To address this issue, based on the Total organic matter content and XRD experimental results of core samples, the deep shale reservoirs of the Qiongzhusi Formation were divided into five lithofacies (organic rich silty shale, organic rich calcium containing silty shale, organic poor silty shale, organic poor calcium containing silty shale, and organic poor clayey shale). Based on the triangulation of lithofacies and analysis of lithofacies characteristics, a workflow and model for identifying lithofacies in deep shale gas reservoirs were established using neural network clustering analysis theory. The confusion matrix results of the testing, validation, and training datasets were all greater than 88%, indicating high recognition accuracy. The identification of lithology in Well Z2 using its model is more accurate and efficient than traditional lithology methods, which is helpful for the efficient development of deep shale gas reservoirs in the study area and provides new ideas for lithology identification research in deep ultra deep shale gas reservoirs.

Key words: Qiongzhusi Formation, deep shale reservoirs, classification of shale rock facies, workflow, neural network clustering analysis