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Global Geology 2020, 23(3) 155-165 DOI: 10.3969/j.issn.1673-9736.2020.03.03 ISSN: 1673-9736 CN: 22-1371/P

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	shale
	rock-physics model
	BP neural network
	prediction of shear wave velocity
	本文作者相关文章
	ZHU Hai
	XU Cong
	LI Peng
	LIU Cai
	PubMed
	Article by Zhu H
	Article by Xu C
	Article by Li P
	Article by Liu C

Structure analysis of shale and prediction of shear wave velocity based on petrophysical model and neural network

ZHU Hai¹, XU Cong², LI Peng¹, LIU Cai¹

1. College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China;
2. Northeast Electric Power Design Institute Co., Ltd of China Power Engineering Consulting Group, Changchun 130026, China

摘要： Accurate shear wave velocity is very important for seismic inversion. However, few researches in the shear wave velocity in organic shale have been carried out so far. In order to analyze the structure of organic shale and predict the shear wave velocity, the authors propose two methods based on petrophysical model and BP neural network respectively, to calculate shear wave velocity. For the method based on petrophysics model, the authors discuss the pore structure and the space taken by kerogen to construct a petrophysical model of the shale, and establish the quantitative relationship between the P-wave and S-wave velocities of shale and physical parameters such as pore aspect ratio, porosity and density. The best estimation of pore aspect ratio can be obtained by minimizing the error between the predictions and the actual measurements of the P-wave velocity. The optimal porosity aspect ratio and the shear wave velocity are predicted. For the BP neural network method that applying BP neural network to the shear wave prediction, the relationship between the physical properties of the shale and the elastic parameters is obtained by training the BP neural network, and the P-wave and S-wave velocities are predicted from the reservoir parameters based on the trained relationship. The above two methods were tested by using actual logging data of the shale reservoirs in the Jiaoshiba area of Sichuan Province. The predicted shear wave velocities of the two methods match well with the actual shear wave velocities, indicating that these two methods are effective in predicting shear wave velocity.

关键词： shale rock-physics model BP neural network prediction of shear wave velocity

Structure analysis of shale and prediction of shear wave velocity based on petrophysical model and neural network

ZHU Hai¹, XU Cong², LI Peng¹, LIU Cai¹

Abstract: Accurate shear wave velocity is very important for seismic inversion. However, few researches in the shear wave velocity in organic shale have been carried out so far. In order to analyze the structure of organic shale and predict the shear wave velocity, the authors propose two methods based on petrophysical model and BP neural network respectively, to calculate shear wave velocity. For the method based on petrophysics model, the authors discuss the pore structure and the space taken by kerogen to construct a petrophysical model of the shale, and establish the quantitative relationship between the P-wave and S-wave velocities of shale and physical parameters such as pore aspect ratio, porosity and density. The best estimation of pore aspect ratio can be obtained by minimizing the error between the predictions and the actual measurements of the P-wave velocity. The optimal porosity aspect ratio and the shear wave velocity are predicted. For the BP neural network method that applying BP neural network to the shear wave prediction, the relationship between the physical properties of the shale and the elastic parameters is obtained by training the BP neural network, and the P-wave and S-wave velocities are predicted from the reservoir parameters based on the trained relationship. The above two methods were tested by using actual logging data of the shale reservoirs in the Jiaoshiba area of Sichuan Province. The predicted shear wave velocities of the two methods match well with the actual shear wave velocities, indicating that these two methods are effective in predicting shear wave velocity.

Keywords: shale rock-physics model BP neural network prediction of shear wave velocity

收稿日期 2019-12-23 修回日期 2020-02-25 网络版发布日期

DOI: 10.3969/j.issn.1673-9736.2020.03.03

基金项目:

Supported by projects of National Natural Science Foundation of China (No.41874125, No. 41430322).

通讯作者: LI Peng

作者简介:

作者Email: pengl@jlu.edu.cn

参考文献：

Bai J Y, Song Z X, Su L,et al. 2012. Error analysis of shear wave velocity prediction based on Xu-White model.Chinese Journal of Geophysics,55(2):589-595. (in Chinese with English abstract)
Berryman J G. 1980. Long-wavelength propagation in compo-site elastic media. Ⅱ. ellipsoidal inclusions.The Journal of the Acoustical Society of America,68(6):1820-1831.
Biot M A. 1956. Theory of propagation of elastic waves in a fluid-saturated porous solid Ⅰ. low-frequency range.The Journal of the Acoustical Society of America,28(2):168-175.
Castagna J P, Greenberg M L. 1992. Shear-wave velocity estimation in porous rocks:theoretical formulation, preliminary verification and applications.Geophysical Prospecting,40(2):195-209.
Chen S B, Xia X H, Qin Y,et al. 2013. Classification of pore structure of shale gas reservoirs in the Longmaxi Formation in the South Sichuan Basin.Journal of China Coal Society, (5):42-47. (in Chinese with English abstract)
Cho K, van Merrienboer B, Gulcehre C,et al. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation.Computer Science,10(2):1-13.
Chu Z H, Lu B P. 1995. A new method for estimating shear wave velocity in formation. Well Logging Technology,19(5):313-318, 339. (in Chinese with English abstract)
De Sousa M C, De Figueiredo José J S, Da Silva C B,et al. 2019. Prediction of S-wave velocity by a hybrid model based on the Greenberg-Castagna equation.Journal of Petroleum Science and Engineering,172(1):303-313.
Deng J X, Wang H, Zhou H,et al. 2015. Microstructure, seismic petrophysical characteristics and modeling of the Longmaxi Formation shale. Chinese Journal of Geophysics,(6):287-300. (in Chinese with English abstract)
Gassmann F. 1951. Elastic waves through a packing of spheres.Geophysics,16(4):673-685.
Guo Z Q, Li X Y, Liu C. 2014. Anisotropy parameters estimate and rock physics analysis for the Barnett Shale. Journal of Geophysics and Engineering,11(6):1-10.
Han D H, Nur A, Morgan D. 1986. Effects of porosity and clay content on wave velocities in sandstones.Geophysics,51(11):2093-2107.
Hill R. 1965. A self-consistent mechanics of composite mate-rials.Journal of the Mechanics & Physics of Solids,13(4):213-222.
Hu Q, Chen X H, Li J Y. 2014. Shear wave velocity prediction of shale gas reservoirs based on anisotropic petrophy-sical model.Geophysical Prospecting for Petroleum,53(3):254-261. (in Chinese with English abstract)
Kuster G T, Toksöz M N. 1974. Velocity and attenuation of seismic waves in two-phase media:part Ⅰ. theoretical formulations.Geophysics,39(5):587-606.
Li X, Xiang S, Zhu P,et al. 2015. Establishing a dynamic self-adaptation learning algorithm of the BP neural network and its applications.International Journal of Bifurcation & Chaos,25(14):514-526.
Parvizi S, Kharrat R, Asef M R,et al. 2015. Prediction of the shear wave velocity from compressional wave velocity for Gachsaran Formation.Acta Geophysica,63(5):1231-1243.
Sun F L, Yang C C, Ma S H,et al. 2008. Shear wave velocity prediction method.Progress in Geophysics, (2):176-180. (in Chinese with English abstract)
Wang F P, Hamme U. 2010. Effects of petrophysical factors on Haynesville fluid flow and production.World Oil,231(4):79-82. (in Chinese with English abstract)
Wang X G. 2013. Application of self-adaptive BP neural network to the prediction of shear wave velocity.Lithologic Reservoirs,25(5):86-88.
Xu S, White R E.1996. A physical model for shear-wave velocity prediction.Geophysical Prospect,44(4):687-717.
Xu S Y, Payne M A. 2009. Modeling elastic properties in carbonates rocks.The Leading Edge,28(1):66-74.
Yin X Y, Li L. 2015. Method of P-wave and S-wave velocity inversion based on petrophysical model.Geophysical Prospecting for Petroleum,54(3):249-253. (in Chinese with English abstract)
Yun M H, Gao J, He Y S,et al. 2004. Relations between reservoir velocity and density and porosity, shale content, and water saturation.Advances in Exploration Geophysics,27(2):104-107. (in Chinese with English abstract)

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