Cross-Well Lithology Identification Based on Wavelet Transform and Adversarial Learning

Longxiang Sun, Zerui Li, Kun Li (), Haining Liu, Ge Liu and Wenjun Lv
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Longxiang Sun: AHU-IAI AI Joint Laboratory, Anhui University, Hefei 230601, China
Zerui Li: Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China
Kun Li: Institute of Advanced Technology, University of Science and Technology of China, Hefei 230031, China
Haining Liu: Geophysical Research Institute, SINOPEC Shengli Oilfield Company, Dongying 257022, China
Ge Liu: Geophysical Research Institute, SINOPEC Shengli Oilfield Company, Dongying 257022, China
Wenjun Lv: Institute of Advanced Technology, University of Science and Technology of China, Hefei 230031, China

Energies, 2023, vol. 16, issue 3, 1-17

Abstract: For geological analysis tasks such as reservoir characterization and petroleum exploration, lithology identification is a crucial and foundational task. The logging lithology identification tasks at this stage generally build a lithology identification model, assuming that the logging data share an independent and identical distribution. This assumption, however, does not hold among various wells due to the variations in depositional conditions, logging apparatus, etc. In addition, the current lithology identification model does not fully integrate the geological knowledge, meaning that the model is not geologically reliable and easy to interpret. Therefore, we propose a cross-domain lithology identification method that incorporates geological information and domain adaptation. This method consists of designing a named UAFN structure to better extract the semantic (depth) features of logging curves, introducing geological information via wavelet transform to improve the model’s interpretability, and using dynamic adversarial domain adaptation to solve the data-drift issue cross-wells. The experimental results show that, by combining the geological information in wavelet coefficients with semantic information, more lithological features can be extracted in the logging curve. Moreover, the model performance is further improved by dynamic domain adaptation and wavelet transform. The addition of wavelet transform improved the model performance by an average of 6.25%, indicating the value of the stratigraphic information contained in the wavelet coefficients for lithology prediction.

Keywords: lithology identification; cross-domain; wavelet transform; adversarial learning; semantic segmentation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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