基于叠前概率反演的致密砂岩甜点直接预测方法

doi:10.13810/j.cnki.issn.1000-7210.2023.05.018

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Abstract Pre-stack seismic inversion is an important method to obtain spatial distribution characteristics of several elastic parameters and identify sweet spots in tight sandstone.As sweet spots of tight sandstone are all located in the inner sandstone, and sand bodies and sweet spots often have different sensitive parameters, inversion parameters usually need to be converted, which reduces the inversion accuracy and efficiency.Thus, this paper proposes a direct prediction method of sweet spots in tight sandstone based on Bayesian probability inversion.Firstly, the Zeoppritz approximation equation of lithology and sensitive elastic parameters of sweet spots is derived, and then the convolution model is combined to construct the relationship between sensitive parameters and seismic records.Secondly, under the framework of Bayesian theory, the posterior probability distribution expression of sensitive parameters is established, and the Markov chain-Monte Carlo method is adopted to sample the posterior probability distribution, with the correlation between inversion parameters during the sampling being fully considered.Finally, the improved Bayesian probability inversion method is employed to directly predict the sweet spots in tight sandstone.The proposed method introduces a sampling method based on conditional probability distribution to constrain the sampling space of inversion parameters and improve prediction efficiency and accuracy.The feasibility of the method is verified by the model data and actual data.

Key words： sweet spot pre-stack probability inversion Markov chain-Monte Carlo algorithm conditional probability distribution

Received: 25 November 2022

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P631

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	Articles by authors
	ZHAO Chen
	JIN Fengming
	HAN Guomeng
	GUO Shuwen
	XING Xing
	LIU Hongzhou

Cite this article:

ZHAO Chen,JIN Fengming,HAN Guomeng等. Direct prediction of sweet spots in sandstone reservoirs based on pre-stack probability inversion[J]. OGP, 2023, 58(5): 1211-1219,1230.

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http://www.ogp-cn.com/EN/10.13810/j.cnki.issn.1000-7210.2023.05.018 OR http://www.ogp-cn.com/EN/Y2023/V58/I5/1211

[1]	杨升宇, 张金川, 黄卫东, 等.吐哈盆地柯柯亚地区致密砂岩气储层"甜点"类型及成因[J].石油学报, 2013, 34(2):272-282.YANG Shengyu, ZHANG Jinchuan, HUANG Weidong, et al."Sweet spot" types of reservoirs and genesis of tight sandstone gas in Kekeya area, Turpan-Hami Basin[J].Acta Petrolei Sinica, 2013, 34(2):272-282.
[2]	TITCHKOSKY K, THOMPSON R.Picking the sweet spot using rock physics[C].SEG Technical Program Expanded Abstracts, 2008, 27:264-268.
[3]	BULAND A, OMRE H.Bayesian linearized AVO inversion[J].Geophysics, 2003, 68(1):185-198.
[4]	ULVMOEN M, OMRE H.Improved resolution in Bayesian lithology/fluid inversion from prestack seismic data and well observations:Part 1, Methodology[J].Geophysics, 2010, 75(2):21-35.
[5]	印兴耀, 曹丹平, 王保丽, 等.基于叠前地震反演的流体识别方法研究进展[J].石油地球物理勘探, 2014, 49(1):22-34, 46.YIN Xingyao, CAO Danping, WANG Baoli, et al.Research progress of fluid discrimination with pre-stack seismic inversion[J].Oil Geophysical Prospecting, 2014, 49(1):22-34, 46.
[6]	张洪学, 印兴耀, 李坤, 等.利用五维数据直接提取裂缝型储层参数[J].石油地球物理勘探, 2023, 58(2):369-380.ZHANG Hongxue, YIN Xingyao, LI Kun, et al.Direct extraction of fractured reservoirs' parameters based on five-dimensional data[J].Oil Geophysical Prospecting, 2023, 58(2):369-380.
[7]	GRANA D.Joint facies and reservoir properties inversion[J].Geophysics, 2018, 83(3):M15-M24.
[8]	李坤, 印兴耀.混合概率模型驱动的叠前地震反演方法[J].石油地球物理勘探, 2020, 55(4):839-853.LI Kun, YIN Xingyao.Prestack seismic inversion driven by mixture probabilistic models[J].Oil Geophysical Prospecting, 2020, 55(4):839-853.
[9]	周爽爽, 印兴耀, 裴松, 等.地震波形约束的蒙特卡洛-马尔科夫链随机反演方法[J].石油地球物理勘探, 2021, 56(3):543-554, 592.ZHOU Shuangshuang, YIN Xingyao, PEI Song, et al.Monte Carlo-Markov Chain stochastic inversion constrained by seismic waveform[J].Oil Geophysical Prospecting, 2021, 56(3):543-554, 592.
[10]	METROPOLIS N, ROSENBLUTH A W, ROSENBLUTH M N, et a1.Equation of state calculations by fast computing machines[J].The Journal of Chemical Physics, 1953, 21(6):1087-1092.
[11]	GILKS W R, ROBERTS G O, SAHU S K.Adaptive Markov Chain Monte Carlo through regeneration[J].Journal of the American Statistical Association, 1998, 93(443):1045-1054.
[12]	PAN X, ZHANG G, CHEN H, et al.MCMC-based nonlinear EIVAZ inversion driven by rock physics[J].Journal of Geophysics and Engineering, 2017, 14(2):368-379.
[13]	HASTINGS W K.Monte Carlo sampling methods using Markov chains and their applications[J].Biometrika, 1970, 57(1):97-109.
[14]	MALINVERNO A.Parsimonious Bayesian Markov chain Monte Carlo inversion in a nonlinear geophysical problem[J].Geophysical Journal International, 2002, 151(3):675-688.
[15]	CHEN J, KEMNA A, HUBBARD S S.A comparison between Gauss-Newton and Markov-chain Monte Carlo-based methods for inverting spectral induced-polarization data for Cole-Cole parameters[J].Geophysics, 2008, 73(6):F247-F259.
[16]	PAN X.Zeroppritz-based nonlinear AVO inversion using AMDR-MCMC method[C].SEG Technical Program Expanded Abstracts, 2016, 35:572-576.
[17]	SEN M K, BISWAS R.Transdimensional seismic inversion using the reversible jump Hamiltonian Monte Carlo algorithm[J].Geophysics, 2017, 82(3):R119-R134.
[18]	ZHU D, GIBSON R.Seismic inversion and uncertainty quantification using transdimensional Markov chain Monte Carlo method[J].Geophysics, 2018, 83(4), R321-R334.
[19]	张广智, 赵晨, 涂奇催, 等.基于量子退火Metropolis-Hastings算法的叠前随机反演[J].石油地球物理勘探, 2018, 53(1):153-160.ZHANG Gangzhi, ZHAO Chen, TU Qicui, et al.Prestack stochastic inversion based on the quantum annealing Metropolis-Hastings algorithm[J].Oil Geophysical Prospecting, 2018, 53(1):153-160.
[20]	GRANA D, ROSSA E D.Probabilistic petrophysical-properties estimation integrating statistical rock physics with seismic inversion[J].Geophysics, 2010, 75(3):O21-O37.
[21]	AKI K, RICHARDS P G.Quantitative Seismology:Theory and Methods[M].W.H.Freeman, San Francisco, 1980, 144-154.
[22]	ZHAO C, ZHANG G, ZHANG J.Probabilistic inversion for compressional modulus and shear modulus based on QA-MCMC algorithm with joint probability distribution[J].Journal of Applied Geophysics, 2020, 178:104070.
[23]	PAN X, ZHANG G, CHEN H, et al.MCMC-based AVAZ direct inversion for fracture weaknesses[J].Journal of Applied Geophysics, 2017, 138:50-61.
[24]	张广智, 王丹阳, 印兴耀, 等.基于MCMC的叠前地震反演方法研究[J].地球物理学报, 2011, 54(11):2926-2932.ZHANG Guangzhi, WANG Danyang, YIN Xingyao, et al.Study on prestack seismic inversion using Markov Chain Monte Carlo[J].Chinese Journal of Geophysics, 2011, 54(11):2926-2932.
[25]	张广智, 杜炳毅, 李海山, 等.页岩气储层纵横波叠前联合反演方法[J].地球物理学报, 2014, 57(12):4141-4149.ZHANG Guangzhi, DU Bingyi, LI Haishan, et al.The method of joint pre-stack inversion of PP and P-SV waves in shale gas reservoirs[J].Chinese Journal of Geophysics, 2014, 57(12):4141-4149.
[26]	李坤.相驱动叠前地震概率化反演方法研究[D].山东青岛:中国石油大学(华东), 2019.LI Kun.Prestack Seismic Probabilistic Inversion Driven by Facies[D].China University of Petroleum(East China), Qingdao, Shandong, 2019.