Physics-guided deep learning for predicting geological drilling risk of wellbore instability using seismic attributes data
文献类型：期刊论文
| 作者 | Geng, Zhi1,2; Wang, Yanfei1,2,3 |
| 刊名 | ENGINEERING GEOLOGY
 |
| 出版日期 | 2020-12-20 |
| 卷号 | 279页码:11 |
| 关键词 | Physics-guided deep learning Wellbore instability Seismic attribute Drilling risk |
| ISSN号 | 0013-7952 |
| DOI | 10.1016/j.enggeo.2020.105857 |
| 英文摘要 | Wellbore instability is a major safety and environmental concern in both onshore and offshore drilling. Geological drilling risk assessment for wellbore collapse is critical for the optimization of well plans, and to reduce potential costs before drilling. In this work, we propose a physics-guided deep learning approach to predict wellbore instability using seismic attributes data. We first trained an auto-encoder to extract latent representation (five principle features) from 17 typical seismic attributes, and then we introduced a regularization term, based on geomechanics in the objective function to train a neural network. As long as there is no significant over-pressure in the formation, the physics-based regularization term indicating wellbore instability risk is a function of neutron porosity, and of the true vertical depth obtained from well logging data. In this way, we combined drill log data for five wells, as prior information, with latent seismic attribute representations, to train the neural network. After training, our approach needed only seismic data to predict wellbore instability risk in new locations, and our case study showed that the physics-based regularizer, with an appropriate weight, prevented overfitting to training data and enhanced the generalization accuracy of the neural network (by similar to 4%) in two new test wells. We argue that statistical correlations between seismic attributes and rock properties are algorithm dependent, and have to be treated cautiously in the absence of a base of petrophysical reasoning. The physics-guided deep learning method presented here has potential application for the quantification of geology-based wellbore instability risk before drilling. |
| 资助项目 | National Key R&D Program of the Ministry of Science and Technology of China[2018YFC1504203] ; National Key R&D Program of the Ministry of Science and Technology of China[2018YFC0603500] ; Key Research Program of the Institute of Geology & Geophysics, CAS[IGGCAS201903] ; Original Innovation Program of CAS[ZDBS-LYDQC003] |
| WOS研究方向 | Engineering ; Geology |
| 语种 | 英语 |
| WOS记录号 | WOS:000597310200006 |
| 出版者 | ELSEVIER |
| 资助机构 | National Key R&D Program of the Ministry of Science and Technology of China ; National Key R&D Program of the Ministry of Science and Technology of China ; National Key R&D Program of the Ministry of Science and Technology of China ; National Key R&D Program of the Ministry of Science and Technology of China ; Key Research Program of the Institute of Geology & Geophysics, CAS ; Key Research Program of the Institute of Geology & Geophysics, CAS ; Key Research Program of the Institute of Geology & Geophysics, CAS ; Key Research Program of the Institute of Geology & Geophysics, CAS ; Original Innovation Program of CAS ; Original Innovation Program of CAS ; Original Innovation Program of CAS ; Original Innovation Program of CAS ; National Key R&D Program of the Ministry of Science and Technology of China ; National Key R&D Program of the Ministry of Science and Technology of China ; National Key R&D Program of the Ministry of Science and Technology of China ; National Key R&D Program of the Ministry of Science and Technology of China ; Key Research Program of the Institute of Geology & Geophysics, CAS ; Key Research Program of the Institute of Geology & Geophysics, CAS ; Key Research Program of the Institute of Geology & Geophysics, CAS ; Key Research Program of the Institute of Geology & Geophysics, CAS ; Original Innovation Program of CAS ; Original Innovation Program of CAS ; Original Innovation Program of CAS ; Original Innovation Program of CAS ; National Key R&D Program of the Ministry of Science and Technology of China ; National Key R&D Program of the Ministry of Science and Technology of China ; National Key R&D Program of the Ministry of Science and Technology of China ; National Key R&D Program of the Ministry of Science and Technology of China ; Key Research Program of the Institute of Geology & Geophysics, CAS ; Key Research Program of the Institute of Geology & Geophysics, CAS ; Key Research Program of the Institute of Geology & Geophysics, CAS ; Key Research Program of the Institute of Geology & Geophysics, CAS ; Original Innovation Program of CAS ; Original Innovation Program of CAS ; Original Innovation Program of CAS ; Original Innovation Program of CAS ; National Key R&D Program of the Ministry of Science and Technology of China ; National Key R&D Program of the Ministry of Science and Technology of China ; National Key R&D Program of the Ministry of Science and Technology of China ; National Key R&D Program of the Ministry of Science and Technology of China ; Key Research Program of the Institute of Geology & Geophysics, CAS ; Key Research Program of the Institute of Geology & Geophysics, CAS ; Key Research Program of the Institute of Geology & Geophysics, CAS ; Key Research Program of the Institute of Geology & Geophysics, CAS ; Original Innovation Program of CAS ; Original Innovation Program of CAS ; Original Innovation Program of CAS ; Original Innovation Program of CAS |
| 源URL | [http://ir.iggcas.ac.cn/handle/132A11/99938]  |
| 专题 | 地质与地球物理研究所_中国科学院油气资源研究重点实验室 |
| 通讯作者 | Wang, Yanfei |
| 作者单位 | 1.Chinese Acad Sci, Innovat Acad Earth Sci, Beijing 100029, Peoples R China 2.Chinese Acad Sci, Inst Geol & Geophys, Key Lab Petr Resources Res, Beijing 100029, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Geng, Zhi,Wang, Yanfei. Physics-guided deep learning for predicting geological drilling risk of wellbore instability using seismic attributes data[J]. ENGINEERING GEOLOGY,2020,279:11. |
| APA | Geng, Zhi,&Wang, Yanfei.(2020).Physics-guided deep learning for predicting geological drilling risk of wellbore instability using seismic attributes data.ENGINEERING GEOLOGY,279,11. |
| MLA | Geng, Zhi,et al."Physics-guided deep learning for predicting geological drilling risk of wellbore instability using seismic attributes data".ENGINEERING GEOLOGY 279(2020):11. |
入库方式： OAI收割
来源：地质与地球物理研究所
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