Anisotropic Bayesian linearized stochastic seismic inversion with multi-parameter decoupling

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Bo Yu, Ying Shi, Yukun Tian, Hui Zhou, Zhanqing Yu, Yuanpeng Zhang, Weihong Wang
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引用次数: 0

Abstract

Shale oil reservoir emerges as a significant unconventional energy source, commonly predicted by anisotropic seismic inversion. Considering the intricate nature of shale oil reservoirs, it becomes imperative to consider uncertainties during anisotropic inversion. An effective approach to address this involves stochastic inversion, specifically the anisotropic Bayesian linearized inversion (ABLI), which characterizes statistical and spatial correlations of subsurface parameters through a crucial multivariate correlation matrix constructed through geostatistics. However, an inevitable challenge in stochastic inversion arises from interference during the calibration of statistical and spatial correlations of subsurface parameters. This challenge becomes particularly pronounced in anisotropic inversion, heightened by the multitude of involved model parameters. Existing decorrelation approaches primarily address statistical correlation, neglecting the impact of spatial correlation. To tackle this issue, a novel multi-parameter decoupling strategy is proposed, formulating decoupling anisotropic Bayesian linearized inversion (D-ABLI). D-ABLI introduces an advanced decorrelation approach, and uses principal component analysis (PCA) to simultaneously eliminate impact of statistical and spatial correlations on ABLI. The decoupling enhances the inversion accuracy of model parameters in ABLI, particularly for density and anisotropic parameters. The theoretical underpinnings of the decoupling strategy are demonstrated to be reasonable, and the effectiveness of D-ABLI is proved through a theoretical data test and a field data test regarding shale oil reservoirs. The D-ABLI results offer the capability to estimate fracture density accurately and unveil the distribution of shale oil.
多参数解耦的各向异性贝叶斯线性化随机地震反演
页岩油藏是一种重要的非常规能源,通常通过各向异性地震反演进行预测。考虑到页岩油藏错综复杂的性质,在各向异性反演过程中必须考虑不确定性。解决这一问题的有效方法是随机反演,特别是各向异性贝叶斯线性化反演(ABLI),该方法通过地质统计学构建的重要多元相关矩阵来描述地下参数的统计和空间相关性。然而,随机反演中不可避免的挑战来自校准地下参数的统计和空间相关性过程中的干扰。在各向异性反演中,这一挑战尤为突出,因为涉及的模型参数众多。现有的去相关性方法主要解决统计相关性问题,忽略了空间相关性的影响。为解决这一问题,提出了一种新颖的多参数解耦策略,即解耦各向异性贝叶斯线性化反演(D-ABLI)。D-ABLI 引入了先进的去相关方法,并使用主成分分析(PCA)同时消除统计和空间相关性对 ABLI 的影响。解耦增强了 ABLI 中模型参数的反演精度,尤其是密度和各向异性参数。解耦策略的理论基础是合理的,D-ABLI 的有效性通过理论数据测试和页岩油藏现场数据测试得到了证明。D-ABLI 结果能够准确估计裂缝密度,揭示页岩油的分布。
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来源期刊
Accounts of Chemical Research
Accounts of Chemical Research 化学-化学综合
CiteScore
31.40
自引率
1.10%
发文量
312
审稿时长
2 months
期刊介绍: Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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