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Comparison of hydraulic and tracer tomography for discrete fracture network inversion

  • Fractures serve as highly conductive preferential flow paths for fluids in rocks, which are difficult to exactly reconstruct in numerical models. Especially, in low-conductive rocks, fractures are often the only pathways for advection of solutes and heat. The presented study compares the results from hydraulic and tracer tomography applied to invert a theoretical discrete fracture network (DFN) that is based on data from synthetic cross-well testing. For hydraulic tomography, pressure pulses in various injection intervals are induced and the pressure responses in the monitoring intervals of a nearby observation well are recorded. For tracer tomography, a conservative tracer is injected in different well levels and the depth-dependent breakthrough of the tracer is monitored. A recently introduced transdimensional Bayesian inversion procedure is applied for both tomographical methods, which adjusts the fracture positions, orientations, and numbers based on given geometrical fracture statistics. The used Metropolis-Hastings-GreenFractures serve as highly conductive preferential flow paths for fluids in rocks, which are difficult to exactly reconstruct in numerical models. Especially, in low-conductive rocks, fractures are often the only pathways for advection of solutes and heat. The presented study compares the results from hydraulic and tracer tomography applied to invert a theoretical discrete fracture network (DFN) that is based on data from synthetic cross-well testing. For hydraulic tomography, pressure pulses in various injection intervals are induced and the pressure responses in the monitoring intervals of a nearby observation well are recorded. For tracer tomography, a conservative tracer is injected in different well levels and the depth-dependent breakthrough of the tracer is monitored. A recently introduced transdimensional Bayesian inversion procedure is applied for both tomographical methods, which adjusts the fracture positions, orientations, and numbers based on given geometrical fracture statistics. The used Metropolis-Hastings-Green algorithm is refined by the simultaneous estimation of the measurement error’s variance, that is, the measurement noise. Based on the presented application to invert the two-dimensional cross-section between source and the receiver well, the hydraulic tomography reveals itself to be more suitable for reconstructing the original DFN. This is based on a probabilistic representation of the inverted results by means of fracture probabilities.zeige mehrzeige weniger

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Metadaten
Verfasserangaben:Lisa Maria Ringel, Márk SomogyváriGND, Mohammadreza JalaliORCiD, Peter BayerORCiDGND
DOI:https://doi.org/10.3390/geosciences9060274
ISSN:2076-3263
Titel des übergeordneten Werks (Englisch):Geosciences
Verlag:MDPI
Verlagsort:Basel
Publikationstyp:Wissenschaftlicher Artikel
Sprache:Englisch
Datum der Erstveröffentlichung:21.06.2019
Erscheinungsjahr:2019
Datum der Freischaltung:04.02.2021
Freies Schlagwort / Tag:Bayesian inversion; DFN; fracture; heterogeneity; hydraulic tomography; hydrogeophysics; tracer tomography
Band:9
Ausgabe:6
Seitenanzahl:17
Fördernde Institution:German Research Foundation (DFG)German Research Foundation (DFG) [BA-2850-5-1]
Organisationseinheiten:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Mathematik
DDC-Klassifikation:5 Naturwissenschaften und Mathematik / 51 Mathematik / 510 Mathematik
Peer Review:Referiert
Publikationsweg:Open Access / Gold Open-Access
DOAJ gelistet
Lizenz (Deutsch):License LogoCC-BY - Namensnennung 4.0 International
Externe Anmerkung:Zweitveröffentlichung in der Schriftenreihe Postprints der Universität Potsdam : Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe ; 922
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