标题：Application of machine learning in the identification of fluvial-lacustrine lithofacies from well logs: A case study from Sichuan Basin, China

入藏号：WOS:000800563500003

中国科学院文献情报中心期刊分区：工程技术2区TOP（2022）

本校作者：郑栋宇，侯明才，陈安清，钟瀚霆，齐哲，任强，尤加春，马超

来源出版物：JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING  卷: 215  文献号: 110610

出版年：2022

第一地址：成都理工大学

关键词：Machine learning;Fluvial-lacustrine ;lithofacies;XGBoost;Resampling;Well log;Sichuan Basin

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摘要：The lithofacies identification is critical for forecasting sweet spots of hydrocarbon explorations. Well logs are widely used in lithofacies identifications because they are petrophysical measurements of subsurface stratigraphy which reflect lithological successions and depositional processes. The traditional lithofacies identification from well logs is a manual work that is time-consuming and bias-prone. An automated and bias-free method is in demand. To this end, we created a lithofacies dataset of eleven wells with well log records and lithofacies descriptions that were interpreted manually based on facies analysis of drilling cutting descriptions and well logs. Then we developed machine learning models that were trained using the lithofacies dataset of the fluvial-lacustrine Upper Triassic Xujiahe and Lower Jurassic Ziliujing formations in Yuanba Area, northern Sichuan Basin of southwestern China. By employing extreme gradient boosting and resampling algorithms, this machine learning model is efficient and outperforms support vector machine and multiple-layer perceptron, as indicated by its highest accuracy and F1-score of 0.90, the highest AUC of 0.94, as well as the shortest training time. Moreover, the result suggests that resampling is necessary for lithofacies identification with the imbalanced dataset. A combined method of oversampling and undersampling is better than a single resampling method. This study presents a successful application of machine learning in fluvial-lacustrine lithofacies identification from well logs and suggests the great potentiality of machine learning in subsurface hydrocarbon explorations.

文章链接地址: https://www.sciencedirect.com/science/article/pii/S0920410522004855?via%3Dihub