Using machine learning methods to identify coals from drilling and logging-while-drilling LWD data

Ruizhi Zhong, Raymond L. Johnson, Zhongwei Chen

科研成果: 书/报告/会议事项章节会议稿件同行评审

10 引用 (Scopus)

摘要

Accurate coal identification is critical in coal seam gas (CSG) (also known as coalbed methane or CBM) developments because it determines well completion design and directly affects gas production. Density logging using radioactive source tools is the primary tool for coal identification, adding well trips to condition the hole and additional well costs for logging runs. In this paper, machine learning methods are applied to identify coals from drilling and logging-while-drilling (LWD) data to reduce overall well costs. Machine learning algorithms include logistic regression (LR), support vector machine (SVM), artificial neural network (ANN), random forests (RF), and extreme gradient boosting (XGBoost). The precision, recall, and F1 score are used as evaluation metrics. Because coal identification is an imbalanced data problem, the performance on the minority class (i.e., coals) is limited. To enhance the performance on coal prediction, two data manipulation techniques [naive random oversampling technique (NROS) and synthetic minority oversampling technique (SMOTE)] are separately coupled with machine learning algorithms. Case studies are performed with data from six wells in the Surat Basin, Australia. For the first set of experiments (single well experiments), both the training data and test data are in the same well. The machine learning methods can identify coal pay zones for sections with poor or missing logs. It is found that ROP is the most important feature. The second set of experiments (multiple well experiments) use the training data from multiple nearby wells, which can predict coal pay zones in a new well. The most important feature is gamma ray. After placing slotted casings, all wells have over 90% coal identification rates and three wells have over 99% coal identification rates. This indicates that machine learning methods (either XGBoost or ANN/RF with NROS/SMOTE) can be an effective way to identify coal pay zones and reduce coring or logging costs in CSG developments.

源语言英语
主期刊名SPE/AAPG/SEG Asia Pacific Unconventional Resources Technology Conference 2019, APUR 2019
出版商Unconventional Resources Technology Conference (URTEC)
ISBN(电子版)9781613996737
DOI
出版状态已出版 - 2019
已对外发布
活动SPE/AAPG/SEG Asia Pacific Unconventional Resources Technology Conference 2019, APUR 2019 - Brisbane, 澳大利亚
期限: 18 11月 201919 11月 2019

出版系列

姓名SPE/AAPG/SEG Asia Pacific Unconventional Resources Technology Conference 2019, APUR 2019

会议

会议SPE/AAPG/SEG Asia Pacific Unconventional Resources Technology Conference 2019, APUR 2019
国家/地区澳大利亚
Brisbane
时期18/11/1919/11/19

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