Prediction of shear wave velocity in shale reservoirs based on machine learning: a case from the Permian Fengcheng Formation, Mahu Depression

Xiaojun Wang; Zitong Zhang; Yang Zou; Wenjun He; Yi Zhao; Liliang Huang

doi:10.1117/12.2627685

11 March 2022 Prediction of shear wave velocity in shale reservoirs based on machine learning: a case from the Permian Fengcheng Formation, Mahu Depression

Xiaojun Wang, Zitong Zhang, Yang Zou, Wenjun He, Yi Zhao, Liliang Huang

Author Affiliations +

Proceedings Volume 12160, International Conference on Computational Modeling, Simulation, and Data Analysis (CMSDA 2021); 121602E (2022) https://doi.org/10.1117/12.2627685
Event: International Conference on Computational Modeling, Simulation, and Data Analysis (CMSDA 2021), 2021, Sanya, China

Abstract

As an extremely important target for unconventional oil and gas resources exploration at present, shale reservoir differs significantly from conventional clastic and carbonate reservoirs due to their diverse mineral composition, complex pore characteristics, and severe heterogeneity, which makes the conventional theoretical petrophysical models not accurate enough to characterize shale reservoirs. For this reason, machine learning and deep learning methods are introduced to construct a more intelligent petrophysical modeling process, which uses a data-driven approach. And taking the shale reservoirs of the Permian Fengcheng Formation in Mahu Depression of Junggar Basin as an example, we achieve high accuracy Shear wave velocity prediction based on conventional well logs, and the mean relative error (MRE) of prediction is reduced by 2.78-3.88% and the method has good applicability and generalization compared with conventional petrophysical model.

Citation Download Citation

Xiaojun Wang, Zitong Zhang, Yang Zou, Wenjun He, Yi Zhao, and Liliang Huang "Prediction of shear wave velocity in shale reservoirs based on machine learning: a case from the Permian Fengcheng Formation, Mahu Depression", Proc. SPIE 12160, International Conference on Computational Modeling, Simulation, and Data Analysis (CMSDA 2021), 121602E (11 March 2022); https://doi.org/10.1117/12.2627685

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KEYWORDS

Machine learning

Process modeling

Data modeling

Error analysis

Velocity measurements

Visual process modeling

Earth sciences

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