Detecting dynamic causal inference in nonlinear two-phase fracture flow

Journal Article · Thu Feb 16 00:00:00 EST 2017 · Advances in Water Resources

DOI:https://doi.org/10.1016/j.advwatres.2017.02.011· OSTI ID:1476519

Faybishenko, Boris ^[1]

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1. Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); University of California, Berkeley, CA (United States)

Identifying dynamic causal inference involved in flow and transport processes in complex fractured-porous media is generally a challenging task, because nonlinear and chaotic variables may be positively coupled or correlated for some periods of time, but can then become spontaneously decoupled or non-correlated. In his 2002 paper (Faybishenko, 2002), the author performed a nonlinear dynamical and chaotic analysis of time-series data obtained from the fracture flow experiment conducted by Persoff and Pruess (1995), and, based on the visual examination of time series data, hypothesized that the observed pressure oscillations at both inlet and outlet edges of the fracture result from a superposition of both forward and return waves of pressure propagation through the fracture. In the current paper, the author explores an application of a combination of methods for detecting nonlinear chaotic dynamics behavior along with the multivariate Granger Causality (G-causality) time series test. Based on the G-causality test, the author infers that his hypothesis is correct, and presents a causation loop diagram of the spatial-temporal distribution of gas, liquid, and capillary pressures measured at the inlet and outlet of the fracture. The causal modeling approach can be used for the analysis of other hydrological processes, for example, infiltration and pumping tests in heterogeneous subsurface media, and climatic processes, for example, to find correlations between various meteorological parameters, such as temperature, solar radiation, barometric pressure, etc.

• Research Organization: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); USDOE Office of Science (SC), Biological and Environmental Research (BER)
• Grant/Contract Number: AC02-05CH11231
• OSTI ID: 1476519
• Alternate ID(s): OSTI ID: 1550539
• Journal Information: Advances in Water Resources, Journal Name: Advances in Water Resources Journal Issue: C Vol. 106; ISSN 0309-1708
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

54 ENVIRONMENTAL SCIENCES
58 GEOSCIENCES
capillary pressure
causality loop diagram
granger causality
nonlinear dynamics
time series
two-phase fracture flow