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

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. In conclusion, the efficacy of this inversion is demonstrated through a representative example.
Authors:
ORCiD logo [1] ; ORCiD logo [1] ; ORCiD logo [1] ; ORCiD logo [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Report Number(s):
LA-UR-16-20968
Journal ID: ISSN 1932-1864
Grant/Contract Number:
AC52-06NA25396
Type:
Accepted Manuscript
Journal Name:
Statistical Analysis and Data Mining
Additional Journal Information:
Journal Volume: 10; Journal Issue: 5; Conference: Conference on Data Analysis, Santa Fe, NM (United States), 2-4 Mar 2016; Journal ID: ISSN 1932-1864
Publisher:
Wiley
Research Org:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org:
USDOE Laboratory Directed Research and Development (LDRD) Program
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; sequential inversion, multiple datastreams, geophysics, flow, fracture, subsurface modeling, clustering analysis, k-means clustering, Latin hypercube sampling, elbow method.
OSTI Identifier:
1394967