Title: Machine learning for graph-based representations of three-dimensional discrete fracture networks

Abstract

Structural and topological information play a key role in modeling flow and transport through fractured rock in the subsurface. Discrete fracture network (DFN) computational suites such as dfnWorks (Hyman et al. Comput. Geosci. 84, 10–19 2015) are designed to simulate flow and transport in such porous media. Flow and transport calculations reveal that a small backbone of fractures exists, where most flow and transport occurs. Restricting the flowing fracture network to this backbone provides a significant reduction in the network’s effective size. However, the particle-tracking simulations needed to determine this reduction are computationally intensive. Such methods may be impractical for large systems or for robust uncertainty quantification of fracture networks, where thousands of forward simulations are needed to bound system behavior. In this paper, we develop an alternative network reduction approach to characterizing transport in DFNs, by combining graph theoretical and machine learning methods. We consider a graph representation where nodes signify fractures and edges denote their intersections. Using random forest and support vector machines, we rapidly identify a subnetwork that captures the flow patterns of the full DFN, based primarily on node centrality features in the graph. Our supervised learning techniques train on particle-tracking backbone paths found by dfnWorks,more » but run in negligible time compared to those simulations. In conclusion, we find that our predictions can reduce the network to approximately 20% of its original size, while still generating breakthrough curves consistent with those of the original network.« less

Authors:

Valera, Manuel ^[1]; Guo, Zhengyang ^[2]; Kelly, Priscilla ^[3]; Matz, Sean ^[4]; Cantu, Vito Adrian ^[1]; Percus, Allon G. ^[4]; Hyman, Jeffrey D.  ^[5];

• Search DOE PAGES for author "Hyman, Jeffrey D."
• Search DOE PAGES for ORCID "0000-0002-4224-2847"

Srinivasan, Gowri  ^[5];

• Search DOE PAGES for author "Srinivasan, Gowri"
• Search DOE PAGES for ORCID "0000-0002-1178-9647"

Viswanathan, Hari S.  ^[5]

• Search DOE PAGES for author "Viswanathan, Hari S."
• Search DOE PAGES for ORCID "0000-0002-1178-9647"

---

+ Show Author Affiliations

1. San Diego State Univ., San Diego, CA (United States). Computational Science Research Center; Claremont Graduate Univ., Claremont, CA (United States). Inst. of Mathematical Sciences
2. C2FO, Leawood, KS (United States). Data & Decision Sciences
3. an Diego State Univ., San Diego, CA (United States). Computational Science Research Center; Claremont Graduate Univ., Claremont, CA (United States). Inst. of Mathematical Sciences
4. Claremont Graduate Univ., Claremont, CA (United States). Inst. of Mathematical Sciences
5. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Publication Date:

Wed Jan 24 00:00:00 EST 2018

Research Org.:

Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Org.:

USDOE Laboratory Directed Research and Development (LDRD) Program

OSTI Identifier:

1479976

Report Number(s):

LA-UR-17-24300
Journal ID: ISSN 1420-0597

Grant/Contract Number:  

AC52-06NA25396

Resource Type:

Accepted Manuscript

Journal Name:

Computational Geosciences

Additional Journal Information:

Journal Volume: 22; Journal Issue: 3; Journal ID: ISSN 1420-0597

Publisher:

Springer

Country of Publication:

United States

Language:

English

Subject:

97 MATHEMATICS AND COMPUTING; Computer Science; Earth Sciences; Mathematics; Machine learning; Discrete fracture networks; Support vector machines; Random forest Centrality