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arXiv 1606.04464cs.CEcs.NAmath.NAphysics.comp-phphysics.geo-phstat.ML

序贯地球物理与流体反演以表征地下系统中的裂缝网络

Sequential geophysical and flow inversion to characterize fracture networks in subsurface systems

Earth and Environmental Sciences Division, Los Alamos National Laboratory(洛斯阿拉莫斯国家实验室地球与环境科学处)

M. K. Mudunuru, S. Karra, N. Makedonska, T. Chen

AI总结:

本文提出一种非侵入式序贯反演框架,整合地球物理和流体数据以约束地下离散裂缝网络,通过微震数据估计裂缝取向统计学界限,并利用流体数据约束裂缝长度。

摘要:

地下应用包括地热能、地质碳封存、石油和天然气等,通常涉及最大化能量提取或流体存储。由于异质性和各向异性,表征地下环境极为复杂,需要从多种碎片化数据流中估计地下参数的不确定性。本文提出一种非侵入式序贯反演框架,整合地球物理和流体数据以约束地下离散裂缝网络(DFN)。该方法首先利用微震数据估计DFN裂缝取向的统计学界限,通过焦点机制(基于物理的方法)和地震数据的聚类分析(统计方法)结合来估计这些界限。然后,基于流体数据约束裂缝长度。通过一个具有代表性的合成示例验证了这种多物理场序贯反演的有效性。

英文摘要:

Subsurface applications including geothermal, geological carbon sequestration, oil and gas, etc., typically involve maximizing either the extraction of energy or the storage of fluids. Characterizing the subsurface is extremely complex due to heterogeneity and anisotropy. Due to this complexity, there are uncertainties in the subsurface parameters, which need to be estimated from multiple diverse as well as fragmented data streams. In this paper, we present a non-intrusive sequential inversion framework, for integrating data from geophysical and flow sources to constraint subsurface Discrete Fracture Networks (DFN). In this approach, we first estimate bounds on the statistics for the DFN fracture orientations using microseismic data. These bounds are estimated through a combination of a focal mechanism (physics-based approach) and clustering analysis (statistical approach) of seismic data. Then, the fracture lengths are constrained based on the flow data. The efficacy of this multi-physics based sequential inversion is demonstrated through a representative synthetic example.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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