NG004:
Data-driven simulations of dynamic fracture networks





Session ID#: 26927

Session Description:
Quantitative understanding of dynamic behavior of fracture networks and associated flow and transport processes plays a central role in a variety of subsurface applications. This session is devoted to forward and inverse modeling of dynamic fracture networks in the presence of multi-scale data and parametric and structural uncertainty. Specific areas of interest include machine learning, data assimilation, Gaussian processes, reduced-order models, and multi-fidelity simulations. The session will encourage collaboration between scientists from different disciplines, such as mathematical modeling, statistics, network science, field experimentation, and data analytics.
Primary Convener:  Humberto C Godinez, Los Alamos National Laboratory, Los Alamos, NM, United States
Convener:  Daniel M Tartakovsky, Stanford University, Department of Energy Resources Engineering, Stanford, CA, United States
Index Terms:

3275 Uncertainty quantification [MATHEMATICAL GEOPHYSICS]
4499 General or miscellaneous [NONLINEAR GEOPHYSICS]
5104 Fracture and flow [PHYSICAL PROPERTIES OF ROCKS]
8010 Fractures and faults [STRUCTURAL GEOLOGY]

Abstracts Submitted to this Session:

Liangchao Zou1, Ulf HÃ¥kansson2,3 and Vladimir Cvetkovic1, (1)KTH Royal Institute of Technology, Department of Sustainable development, Environmental science and Engineering, Stockholm, Sweden, (2)KTH Royal Institute of Technology, Department of Civil and Architectural Engineering, Stockholm, Sweden, (3)Skanska AB, Stockholm, Sweden
Vladimir Cvetkovic, Royal Institute of Technology, Stockholm, Sweden
Miyuki Miyazawa1, Anna Suzuki1, Hiroyuki Shimizu1, Atsushi Okamoto2, Yasuaki Hiraoka3, Ippei Obayashi3, Takeshi Tsuji4 and Takatoshi Ito1, (1)Tohoku University, Institute of Fluid Science, Sendai, Japan, (2)Tohoku University, Graduate School of Environmental Sciences, Sendai, Japan, (3)Tohoku University, Advanced Institute for Materials Research, Sendai, Japan, (4)Kyushu University, Fukuoka, Japan
Amy Lovell1, Satish Karra2, Daniel O'Malley2, Hari Selvi Viswanathan2 and Gowri Srinivasan2, (1)Michigan State University, East Lansing, MI, United States, (2)Los Alamos National Laboratory, Los Alamos, NM, United States
Humberto C Godinez1, Esteban Rougier1, David Osthus2 and Gowri Srinivasan1, (1)Los Alamos National Laboratory, Los Alamos, NM, United States, (2)Los Alamos National Laboratory, Los Alamos, United States
Gowri Srinivasan1, Hari Selvi Viswanathan1, Satish Karra1, Daniel O'Malley1, Humberto C Godinez2, Aric Hagberg3, David Osthus3 and Jamal Mohd-Yusof3, (1)Los Alamos National Laboratory, Los Alamos, NM, United States, (2)Los Alamos National Lab, Los Alamos, NM, United States, (3)Los Alamos National Laboratory, Los Alamos, United States