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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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Verfasserangaben:Lisa Maria Ringel, Márk Somogyvári, Mohammadreza JalaliORCiD, Peter BayerORCiD
URN:urn:nbn:de:kobv:517-opus4-442616
DOI:https://doi.org/10.25932/publishup-44261
ISSN:1866-8372
Titel des übergeordneten Werks (Deutsch):Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe
Schriftenreihe (Bandnummer):Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe (922)
Publikationstyp:Postprint
Sprache:Englisch
Datum der Erstveröffentlichung:28.05.2020
Erscheinungsjahr:2019
Veröffentlichende Institution:Universität Potsdam
Datum der Freischaltung:28.05.2020
Freies Schlagwort / Tag:Bayesian inversion; DFN; fracture; heterogeneity; hydraulic tomography; hydrogeophysics; tracer tomography
Ausgabe:922
Seitenanzahl:19
Quelle:Geosciences 9 (2019) 6, 274 DOI: 10.3390/geosciences9060274
Organisationseinheiten:Mathematisch-Naturwissenschaftliche Fakultät
DDC-Klassifikation:5 Naturwissenschaften und Mathematik / 55 Geowissenschaften, Geologie / 550 Geowissenschaften
Peer Review:Referiert
Publikationsweg:Open Access
Lizenz (Deutsch):License LogoCC-BY - Namensnennung 4.0 International
Externe Anmerkung:Bibliographieeintrag der Originalveröffentlichung/Quelle
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