Shear wave velocity prediction of complex volcanic reservoirs based on deep learning

Zitong Zhang; Shanwei Hu; Kai Zhang; Qiaoyu Ma; Yanan Jiang

doi:10.1117/12.2627666

11 March 2022 Shear wave velocity prediction of complex volcanic reservoirs based on deep learning

Zitong Zhang, Shanwei Hu, Kai Zhang, Qiaoyu Ma, Yanan Jiang

Proceedings Volume 12160, International Conference on Computational Modeling, Simulation, and Data Analysis (CMSDA 2021); 121602D (2022) https://doi.org/10.1117/12.2627666
Event: International Conference on Computational Modeling, Simulation, and Data Analysis (CMSDA 2021), 2021, Sanya, China

Abstract

Volcanic rock formations, as an important oil and gas resource reservoir, have received the focus of the energy industry in recent years. Shear wave logging is essential geophysical data for the exploration and evaluation of volcanic rock oil and gas reservoirs. Due to the strong nonlinear relationship between reservoir logging parameters and S-wave velocity, the conventional point-to-point machine learning methods can not effectively construct the feature space. Deep learning adds neighborhood information to learn the depth features relationship, and builds the mapping of S-wave velocity and wireline logs with its powerful nonlinear solving capability, achieves S-wave velocity prediction. Taking the volcanic reservoir in Xujiaweizi area of Songliao Basin in Northeast China as an example, thirteen logging parameters sensitive to S-wave velocity are selected, and the S-wave velocity prediction models are based on deep learning methods (represented by CNN, ViT, and MLP-Mixer) are proposed. The research demonstrates that the proposed deep learning models are able to predict S-wave velocity with more precision, and the modeling method can give great significance for the exploration of the volcanic reservoir.

Citation Download Citation

Zitong Zhang, Shanwei Hu, Kai Zhang, Qiaoyu Ma, and Yanan Jiang "Shear wave velocity prediction of complex volcanic reservoirs based on deep learning", Proc. SPIE 12160, International Conference on Computational Modeling, Simulation, and Data Analysis (CMSDA 2021), 121602D (11 March 2022); https://doi.org/10.1117/12.2627666

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KEYWORDS

Machine learning

Data modeling

Feature extraction

Convolution

Earth sciences

Transformers

Visual process modeling

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