岩性油气藏 ›› 2021, Vol. 33 ›› Issue (3): 113–119.doi: 10.12108/yxyqc.20210311

• 勘探技术 • 上一篇    下一篇

基于混合高斯先验分布的地质统计学反演

贺东阳, 李海山, 何润, 王伟   

  1. 中国石油勘探开发研究院 西北分院, 兰州 730020
  • 收稿日期:2020-03-04 修回日期:2020-08-17 发布日期:2021-06-03
  • 作者简介:贺东阳（1991—），男，硕士，主要从事地震储层预测和软件开发方面的研究工作。地址：（730020）甘肃省兰州市城关区雁儿湾路535号。Email：he_dy@petrochina.com.cn。
  • 基金资助:
    中国石油天然气股份有限公司勘探与生产分公司项目“消除强波阻抗地震响应对围岩反射特征影响的方法研究”（编号：kt2020-10-04）资助

Geostatistical inversion based on Gaussian mixture prior distribution

HE Dongyang, LI Haishan, HE Run, WANG Wei   

  1. Research Institute of Petroleum Exploration and Development-Northwest, Lanzhou 730020, China
  • Received:2020-03-04 Revised:2020-08-17 Published:2021-06-03

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摘要： 传统的地质统计学反演利用地质统计学模拟算法来构建模型参数的先验信息，然后在地震数据的约束下利用优化算法来获得模型参数的后验解，通常忽视了岩性对模型参数的影响并且在优化过程中计算量大。为此，将模型参数的先验分布表示为受离散岩性影响的混合高斯分布，将线性混合高斯反演理论与地质统计学的序贯模拟相结合，最后通过序贯采样的方法直接获得模型参数和离散岩性的后验解，避免了优化求解过程，且反演结果具有较好的空间连续性和稳定性。模型测试和实际资料的应用表明该方法具有较好的有效性。

关键词: 地质统计学, 序贯模拟, 混合高斯, 离散岩性

Abstract: Traditional geostatistical inversion usually uses geostatistics simulation algorithms to construct the prior information of the model parameters，and then uses some optimization algorithms to obtain the posterior solutions of the model parameters under the constraints of seismic data， which ignores the influence of lithology on model parameters and requires a lot of calculation in the optimization process. Therefore，the prior distribution of model parameters was expressed as Gaussian mixture distribution influenced by discrete lithology，then the linear Gaussian mixture inversion theory was combined with sequential simulation of geostatistics，and the posterior solutions of the model parameters and discrete lithology were directly obtained by sequential sampling，which avoids the optimization process，and the inversion results have good spatial continuity and stability. Both model tests and the application of actual data show the effectiveness of the method.

Key words: geostatistics, sequential simulation, Gaussian mixture, discrete lithology

中图分类号: 

  • P631.4
[1] COOKE D A, SCHNEIDER W A. Generalized linear inversion of reflection seismic data. Geophysics, 1983, 48(6):665-676.
[2] OLDENBURG D W, SCHEUER T, LEVY S. Recovery of the acoustic impedance from reflection seismograms. Geophysics, 1983, 48(10):1318-1337.
[3] 张义, 尹艳树. 约束稀疏脉冲反演在杜坡油田核三段中的应用. 岩性油气藏, 2015, 27(3):103-107. ZHANG Y, YIN Y S. Application of constrained sparse spike inversion in the third member of Hetaoyuan Formation in Dupo Oilfield. Lithologic Reservoirs, 2015, 27(3):103-107.
[4] ULRYCH T J, SACCHI M D, WOODBURY A. A Bayes tour of inversion:A tutorial. Geophysics, 2001, 66(1):55-69.
[5] TARANTOLA A. Inverse problem theory and methods for model parameter estimation. Philadelphia:Society for Industrial and Applied Mathematics, 2005:32-40.
[6] 丁燕, 杜启振, 刘力辉, 等. 基于纵横波同步联合的孔隙模量三参数AVO反演方法. 岩性油气藏, 2020, 32(1):111-119. DING Y,DU Q Z,LIU L H,et al. Three-parameter AVO inversion method of pore modulus based on PP and PS wave simultaneous joint inversion. Lithologic Reservoirs, 2020, 32(1):111-119.
[7] MOSEGAARD K, TARANTOLA A. Monte Carlo sampling of solutions to inverse problems. Journal of Geophysical Research, 1995, 100(B7):12431-12447.
[8] MALINVERNO A. Parsimonious Bayesian Markov chain Monte Carlo inversion in a nonlinear geophysical problem. Geophysical Journal International, 2002, 151(3):675-688.
[9] 张广智, 王丹阳, 印兴耀, 等. 基于MCMC的叠前地震反演方法研究. 地球物理学报, 2011, 54(11):2926-2932. ZHANG G Z, WANG D Y, YIN X Y, et al. Study on prestack seismic inversion using Markov Chain Monte Carlo. Chinese Journal of Geophysics, 2011, 54(11):2926-2932.
[10] 王辉, 张玉芬. 基于模型的叠前数据多参数非线性反演. 岩性油气藏, 2008, 20(2):108-113. WANG H, ZHANG Y F. Model-based multi-parameters non-linear inversion method in prestack domain. Lithologic Reservoirs, 2008, 20(2):108-113.
[11] BULAND A, OMRE H. Bayesian linearized AVO inversion. Geophysics, 2003, 68(1):185-198.
[12] 滕龙, 程玖兵.贝叶斯框架下联合AVA反演与统计岩石物理的储层参数估计.石油地球物理勘探, 2014, 49(4):729-738. TENG L, CHENG J B. Bayesian petrophysical-properties estimation with AVA inversion and statistical rock physics. Oil Geophysical Prospecting, 2014, 49(4):729-738.
[13] GRANA D, ROSSA E D. Probabilistic petrophysical-properties estimation integrating statistical rock physics with seismic inversion. Geophysics, 2010, 75(3):O21-O37.
[14] GRANA D, MUKERJI T. Sequential Bayesian Gaussian mixture linear inversion of seismic data for elastic and reservoir properties estimation. SEG 2012 Annual Meeting, Las Vegas, 2012.
[15] BORTOLI L, ALABERT F A, HAAS A, et al. Constraining stochastic images to seismic data//Soares A. Geostatistics Tróia' 92. Netherlands:Springer, 1993:325-327.
[16] HAAS A, DUBRULE O. Geostatistical inversion-a sequential method of stochastic reservoir modelling constrained by seismic data. First Break, 1994, 12(11):561-569.
[17] 余振, 王彦春, 何静, 等.基于叠前AVA同步反演和地质统计学反演的高分辨率流体预测方法. 石油地球物理勘探, 2014, 49(3):551-560. YU Z, WANG Y C, HE J, et al. A high resolution approach for fluid prediction based on prestack AVA simultaneous inversion and geostatistical inversion. Oil Geophysical Prospecting, 2014, 49(3):551-560.
[18] SOARES A, DIET J D, GUERREIRO L. Stochastic inversion with a global perturbation. EAGE Conference on Petroleum Geostatistics, 2007.
[19] CAETANO H. Integration of seismic information in reservoir models:Global stochastic inversion. Rijeka:New Technologies in the Oil and Gas Industry, 2012:120-128.
[20] AZEVEDO L, NUNES R, SOARES A, et al. Integration of well data into geostatistical seismic amplitude variation with angle inversion for facies estimation. Geophysics, 2015, 80(6):M113-M128.
[21] 孙瑞莹, 印兴耀, 王保丽, 等. 基于Metropolis抽样的弹性阻抗随机反演. 物探与化探, 2015, 39(1):203-210. SUN R Y, YIN X Y, WANG B L, et al. Stochastic inversion of elastic impedance based on Metropolis sampling algorithm. Geophysical & Geochemical Exploration, 2015, 39(1):203-210.
[22] 王保丽, 印兴耀, 丁龙翔, 等. 基于FFT-MA谱模拟的快速随机反演方法研究. 地球物理学报, 2015, 58(2):664-673. WANG B L, YIN X Y, DING L X, et al. Study of fast stochastic inversion based on FFT-MA spectrum simulation. Chinese Journal of Geophysics, 2015, 58(2):664-673.
[23] YANG X W, ZHU P M. Stochastic seismic inversion based on an improved local gradual deformation method. Computers and Geosciences, 2017, 109:75-86.
[24] GONZÁLEZ E F, MUKERJI T, MAVKO G. Seismic inversion combining rock physics and multiple-point geostatistics. Geophysics, 2008, 73(1):R11-R21.
[25] 刘兴业, 李景叶, 陈小宏, 等. 联合多点地质统计学与序贯高斯模拟的随机反演方法. 地球物理学报, 2018, 61(7):2998-3007. LIU X Y, LI J Y, CHEN X H, et al. A stochastic inversion method integrating multi-point geostatistics and sequential Gaussian simulation. Chinese Journal of Geophysics, 2018, 61(7):2998-3007.
[26] JOURNEL A G, GÓMEZ-HERNANDEZ J J. Stochastic imaging of the Wilmington clastic sequence. SPE Formation Evaluation, 1993, 8(1):33-40.
[27] FRANCIS A M. Understanding stochastic inversion:Part 1. First Break, 2006, 24(11):69-77.
[28] OZ B, DEUTSCH C V, TRAN T T, et al. DSSIM-HR:A FORTRAN 90 program for direct sequential simulation with histogram reproduction. Computers & Geosciences, 2003, 29(1):39-51.
[29] DEUTSCH C V, JOURNEL A G. GSLIB:Geostatistical software library and User's Guide. 2nd. New York:Oxford University Press, 1998:115-145.
[30] HANSEN T M, JOURNEL A G, TARANTOLA A, et al. Linear inverse Gaussian theory and geostatistics. Geophysics, 2006, 71(6):R101-R111.
[31] FIGUEIREDO L P, GRANA D, SANTOS M, et al. Bayesian seismic inversion based on rock-physics prior modeling for the joint estimation of acoustic impedance, porosity and lithofacies. Journal of Computational Physics, 2017, 336:128-142.
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