Research on Prediction Method of Volcanic Rock Shear Wave Velocity Based on Improved Xu–White Model

Hanqing Qiao, Bing Zhang and Cai Liu
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Hanqing Qiao: Institute of Geophysical and Geochemical Exploration, China Geological Survey, Langfang 065000, China
Bing Zhang: College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China
Cai Liu: College of Geo-Exploration Science and Technology, Jilin University, Changchun 130021, China

Energies, 2022, vol. 15, issue 10, 1-15

Abstract: Volcanic rock reservoirs have received extensive attention from scholars all over the world because of their geothermal, mineral, and oil and gas resources. Shear wave velocity is the essential information for AVO (amplitude variation with offset) analysis and the reservoir description of volcanic rocks. However, due to factors such as cost, technical reasons, and so on, shear wave velocity is not provided in many logging data. This paper proposes a shear wave velocity prediction method suitable for the conventional logging of volcanic rocks. Firstly, the Xu–White model is improved. The probability distributions formed by the prior information of the logging area are used to initialize the key petrophysical parameters in the model instead of the fixed parameter value to establish the statistical petrophysical model between the logging curve and shear wave velocity. Then, based on the Bayesian inversion method, the simulated P-wave velocity is matched with the actual P-wave logging data to calculate the key petrophysical parameters, and is then used for S-wave velocity prediction. The method is applied to the actual logging data of the No. 5 structure in Nanpu Sag, eastern China. The prediction effect of shear wave velocity is better than that of the conventional method, indicating the feasibility and effectiveness of this method. This study will provide more accurate shear wave velocity data for the exploration and development of volcanic reservoirs.

Keywords: volcanic reservoir; conventional logging; shear wave velocity prediction; Xu–White model; statistical model; Bayesian inversion (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: 2022
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