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Well Logging Based Lithology Identification Model Establishment Under Data Drift: A Transfer Learning Method

1
School of Geosciences, China University of Petroleum, Qingdao 266580, China
2
Shengli Geophysical Research Institute of SINOPEC, Dongying 257022, China
3
School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
4
Department of Automation, University of Science and Technology of China, Hefei 230027, China
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(13), 3643; https://doi.org/10.3390/s20133643
Received: 29 May 2020 / Revised: 21 June 2020 / Accepted: 23 June 2020 / Published: 29 June 2020
(This article belongs to the Section Intelligent Sensors)
Recent years have witnessed the development of the applications of machine learning technologies to well logging-based lithology identification. Most of the existing work assumes that the well loggings gathered from different wells share the same probability distribution; however, the variations in sedimentary environment and well-logging technique might cause the data drift problem; i.e., data of different wells have different probability distributions. Therefore, the model trained on old wells does not perform well in predicting the lithologies in newly-coming wells, which motivates us to propose a transfer learning method named the data drift joint adaptation extreme learning machine (DDJA-ELM) to increase the accuracy of the old model applying to new wells. In such a method, three key points, i.e., the project mean maximum mean discrepancy, joint distribution domain adaptation, and manifold regularization, are incorporated into extreme learning machine. As found experimentally in multiple wells in Jiyang Depression, Bohai Bay Basin, DDJA-ELM could significantly increase the accuracy of an old model when identifying the lithologies in new wells. View Full-Text
Keywords: lithology identification; domain adaptation; manifold regularization; projected maximum mean discrepancy; extreme learning machine lithology identification; domain adaptation; manifold regularization; projected maximum mean discrepancy; extreme learning machine
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Liu, H.; Wu, Y.; Cao, Y.; Lv, W.; Han, H.; Li, Z.; Chang, J. Well Logging Based Lithology Identification Model Establishment Under Data Drift: A Transfer Learning Method. Sensors 2020, 20, 3643.

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