Research on Lithology Identification Method Based on Stacked Capsule Auto-Encoder Network

Hu, Xiaoqing; Cheng, Leiming; Zhou, Heng; Zhang, Chunlei; Zhang, Xin; Li, Deqi

doi:10.1109/icbaie52039.2021.9389895

2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE) 2021

DOI: 10.1109/icbaie52039.2021.9389895

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Research on Lithology Identification Method Based on Stacked Capsule Auto-Encoder Network

Xiaoqing Hu

Leiming Cheng

Heng Zhou

et al.

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

Sun

Li²,

et al. 2023

Energies

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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.

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

Sun

Li²,

et al. 2023

Energies

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Research on Lithology Identification Method Based on Stacked Capsule Auto-Encoder Network

Cited by 1 publication

References 10 publications

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

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

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