随机地震反演关键参数优选和效果分析(英文)
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摘要
随机地震反演技术是将地质统计理论和地震反演相结合的反演方法,它将地震资料、测井资料和地质统计学信息融合为地下模型的后验概率分布,利用马尔科夫链蒙特卡洛(MCMC)方法对该后验概率分布采样,通过综合分析多个采样结果来研究后验概率分布的性质,进而认识地下情况。本文首先介绍了随机地震反演的原理,然后对影响随机地震反演效果的四个关键参数,即地震资料信噪比、变差函数、后验概率分布的样本个数和井网密度进行分析并给出其优化原则。资料分析表明地震资料信噪比控制地震资料和地质统计规律对反演结果的约束程度,变差函数影响反演结果的平滑程度,后验概率分布的样本个数决定样本统计特征的可靠性,而参与反演的井网密度则影响反演的不确定性。最后通过对比试验工区随机地震反演和基于模型的确定性地震反演结果,指出随机地震反演可以给出更符合地下实际情况的模型。
Stochastic seismic inversion is the combination of geostatistics and seismic inversion technology which integrates information from seismic records, well logs, and geostatistics into a posterior probability density function (PDF) of subsurface models. The Markov chain Monte Carlo (MCMC) method is used to sample the posterior PDF and the subsurface model characteristics can be inferred by analyzing a set of the posterior PDF samples. In this paper, we first introduce the stochastic seismic inversion theory, discuss and analyze the four key parameters: seismic data signal-to-noise ratio (S/N), variogram, the posterior PDF sample number, and well density, and propose the optimum selection of these parameters. The analysis results show that seismic data S/N adjusts the compromise between the influence of the seismic data and geostatistics on the inversion results, the variogram controls the smoothness of the inversion results, the posterior PDF sample number determines the reliability of the statistical characteristics derived from the samples, and well density in? uences the inversion uncertainty. Finally, the comparison between the stochastic seismic inversion and the deterministic model based seismic inversion indicates that the stochastic seismic inversion can provide more reliable information of the subsurface character.
Stochastic seismic inversion is the combination of geostatistics and seismic inversion technology which integrates information from seismic records, well logs, and geostatistics into a posterior probability density function (PDF) of subsurface models. The Markov chain Monte Carlo (MCMC) method is used to sample the posterior PDF and the subsurface model characteristics can be inferred by analyzing a set of the posterior PDF samples. In this paper, we first introduce the stochastic seismic inversion theory, discuss and analyze the four key parameters: seismic data signal-to-noise ratio (S/N), variogram, the posterior PDF sample number, and well density, and propose the optimum selection of these parameters. The analysis results show that seismic data S/N adjusts the compromise between the influence of the seismic data and geostatistics on the inversion results, the variogram controls the smoothness of the inversion results, the posterior PDF sample number determines the reliability of the statistical characteristics derived from the samples, and well density in? uences the inversion uncertainty. Finally, the comparison between the stochastic seismic inversion and the deterministic model based seismic inversion indicates that the stochastic seismic inversion can provide more reliable information of the subsurface character.
引文
Bortoli, L. J., Alabert, F., Haas, A., and Journel, A. G., 1993, Constraining stochastic images to seismic data: Soares A., Geostatistics Trois, Volume 1, Dordrecht, Kluwer Academic Publ, 325 – 338.
Fugro-Jason, 2009, Jason Geoscience Workbench SoftwareManual: StatMod MC.
Gan, L. D., Dai, X. F., and Zhang, X., 2011, Research into poststack seismic inversions for thin reservoir characterization: Deterministic and Stochastic 2011SPG/SEG International Geophysical Conference, Shenzhen.
Grijalba-Cuenca, A., Torres-Verdin, C., and van der Made, P., 2000, Geostatistical inversion of 3D seismic data to extrapolate wireline petrophysical variables laterally away from the well: SPE Paper 63283, SPE Annual Technical Conference and Exhibition, Dallas, Texas.
Haas, A., and Dubrule, O., 1994, Geostatistical inversion - a sequential method of stochastic reservoir modeling constrained by seismic data: First Break, 12(11), 561 – 569.
Hansen, T. M., Journel, A. G., Tarantola, A., and Mosegaard, K., 2006, Linear inverse Gaussian theory and geostatistics: Geophysics, 71(6), R101 – R111.
Journel, A., 1989, Fundamentals of geostatistics in five lessons: Volume 8, Short Course in Geology, American Geophysical Union, Washington D.C.
Krige, D. G., 1951, A statistical approach to some mine valuations and allied problems at the Witwatersrand: Masters’s Thesis, University of Witwatersrand, South Africa.
Tarantola, A., 1987, Inverse problem theory: methods for data fitting and model parameter estimation: Elsevier Science Publ. Co., Inc.
Fugro-Jason, 2009, Jason Geoscience Workbench SoftwareManual: StatMod MC.
Gan, L. D., Dai, X. F., and Zhang, X., 2011, Research into poststack seismic inversions for thin reservoir characterization: Deterministic and Stochastic 2011SPG/SEG International Geophysical Conference, Shenzhen.
Grijalba-Cuenca, A., Torres-Verdin, C., and van der Made, P., 2000, Geostatistical inversion of 3D seismic data to extrapolate wireline petrophysical variables laterally away from the well: SPE Paper 63283, SPE Annual Technical Conference and Exhibition, Dallas, Texas.
Haas, A., and Dubrule, O., 1994, Geostatistical inversion - a sequential method of stochastic reservoir modeling constrained by seismic data: First Break, 12(11), 561 – 569.
Hansen, T. M., Journel, A. G., Tarantola, A., and Mosegaard, K., 2006, Linear inverse Gaussian theory and geostatistics: Geophysics, 71(6), R101 – R111.
Journel, A., 1989, Fundamentals of geostatistics in five lessons: Volume 8, Short Course in Geology, American Geophysical Union, Washington D.C.
Krige, D. G., 1951, A statistical approach to some mine valuations and allied problems at the Witwatersrand: Masters’s Thesis, University of Witwatersrand, South Africa.
Tarantola, A., 1987, Inverse problem theory: methods for data fitting and model parameter estimation: Elsevier Science Publ. Co., Inc.
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