Prediction of shear wave velocity in shale reservoirs based on N-BEATS model

Shanwei Hu; Qiaoyu Ma; Kai Zhang; Zitong Zhang

doi:10.1117/12.2627678

11 March 2022 Prediction of shear wave velocity in shale reservoirs based on N-BEATS model

Shanwei Hu, Qiaoyu Ma, Kai Zhang, Zitong Zhang

Proceedings Volume 12160, International Conference on Computational Modeling, Simulation, and Data Analysis (CMSDA 2021); 1216007 (2022) https://doi.org/10.1117/12.2627678
Event: International Conference on Computational Modeling, Simulation, and Data Analysis (CMSDA 2021), 2021, Sanya, China

Abstract

Shear wave velocity (S-wave velocity) is the essential data for rock mechanics parameter prediction and reservoir compressibility evaluation in shale oil and gas sweet spot optimization. Owing to the extremely complex rock components and pore structure of shale reservoirs, it is usually difficult to represent the relationship between well logs and S-wave velocity accurately for theoretical petrophysical models and conventional empirical formulas. Within this context, a novel architecture of S-wave velocity estimation based on N-BEATS model was proposed. It can help improve the estimation accuracy by effectively incorporating sequence features of well logs. To illustrate its performance, a case study for shale reservoir in the Permian Fengcheng Formation in Mahu Sag of Junggar Basin, Xinjiang Oilfield, was performed. Seven kinds of conventional well logs were selected to establish the regression model. Compared with Xu-White model and eleven machine learning methods (MLs) and deep learning methods (DLs), the mean relative error (MRE) of N-BEATS has been reduced to 0.946%. The case study showed that N-BEATS model proposed can achieve better performance and generalization, which indicated its widespread application value to the other oil and gas exploration area.

Citation Download Citation

Shanwei Hu, Qiaoyu Ma, Kai Zhang, and Zitong Zhang "Prediction of shear wave velocity in shale reservoirs based on N-BEATS model", Proc. SPIE 12160, International Conference on Computational Modeling, Simulation, and Data Analysis (CMSDA 2021), 1216007 (11 March 2022); https://doi.org/10.1117/12.2627678

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KEYWORDS

Data modeling

Feature extraction

Machine learning

Earth sciences

Neural networks

Performance modeling

Statistical modeling

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