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Machine Learning Method for TOC Prediction: Taking Wufeng and Longmaxi Shales in the Sichuan Basin, Southwest China as an Example
Geofluids ( IF 1.7 ) Pub Date : 2021-09-26 , DOI: 10.1155/2021/6794213
Jia Rong 1, 2, 3 , Zongyuan Zheng 1, 2, 3 , Xiaorong Luo 1, 2, 3 , Chao Li 1, 2 , Yuping Li 4 , Xiangfeng Wei 4 , Quanchao Wei 4 , Guangchun Yu 4 , Likuan Zhang 1, 2 , Yuhong Lei 1, 2
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The total organic carbon content (TOC) is a core indicator for shale gas reservoir evaluations. Machine learning-based models can quickly and accurately predict TOC, which is of great significance for the production of shale gas. Based on conventional logs, the measured TOC values, and other data of 9 typical wells in the Jiaoshiba area of the Sichuan Basin, this paper performed a Bayesian linear regression and applied a random forest machine learning model to predict TOC values of the shale from the Wufeng Formation and the lower part of the Longmaxi Formation. The results showed that the TOC value prediction accuracy was improved by more than 50% by using the well-trained machine learning models compared with the traditional method in an overmature and tight shale. Using the halving random search cross-validation method to optimize hyperparameters can greatly improve the speed of building the model. Furthermore, excluding the factors that affect the log value other than the TOC and taking the corrected data as input data for training could improve the prediction accuracy of the random forest model by approximately 5%. Data can be easily updated with machine learning models, which is of primary importance for improving the efficiency of shale gas exploration and development.

中文翻译:

TOC预测的机器学习方法:以西南四川盆地五峰页岩和龙马溪页岩为例

总有机碳含量(TOC)是页岩气储层评价的核心指标。基于机器学习的模型可以快速准确地预测TOC,这对页岩气的生产具有重要意义。本文基于四川盆地焦石坝地区 9 口典型井的常规测井、实测 TOC 值等数据,进行贝叶斯线性回归,并应用随机森林机器学习模型,对页岩的 TOC 值进行预测。五峰组及龙马溪组下部。结果表明,与传统的机器学习模型相比,使用训练有素的机器学习模型,TOC值预测精度提高了50%以上。过成熟致密页岩中的方法。使用减半随机搜索交叉验证方法优化超参数可以大大提高模型的构建速度。此外,排除TOC以外的影响log值的因素,将校正后的数据作为输入数据进行训练,可以将随机森林模型的预测精度提高约5%。数据可以使用机器学习模型轻松更新,这对于提高页岩气勘探和开发的效率至关重要。
更新日期:2021-09-27
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