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

Statistical Analysis and Data Mining

Karra, Satish¹  Makedonska, Nataliia¹  Mudunuru, Maruti Kumar¹  Chen, Ting¹ 

[1] Los Alamos National Laboratory Computational Earth Science Group (EES-16), Earth and Environmental Sciences Division Los Alamos New Mexico

关键词: clustering analysis;    elbow method;    flow;    fracture;    geophysics;    k-means clustering;    Latin hypercube sampling;    multiple datastreams;    sequential inversion;    subsurface modeling;   

DOI  :  10.1002/sam.11356

学科分类：社会科学、人文和艺术（综合）

来源: John Wiley & Sons, Inc.

【 摘 要 】

Subsurface applications, including geothermal, geological carbon sequestration, and oil and gas, typically involve maximizing either the extraction of energy or the storage of fluids. Fractures form the main pathways for flow in these systems, and locating these fractures is critical for predicting flow. However, fracture characterization is a highly uncertain process, and data from multiple sources, such as flow and geophysical are needed to reduce this uncertainty. We present a nonintrusive, sequential inversion framework for integrating data from geophysical and flow sources to constrain fracture networks in the subsurface. In this framework, we first estimate bounds on the statistics for the 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 using flow data. The efficacy of this inversion is demonstrated through a representative example.

【 授权许可】

Unknown