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.…
Verfasserangaben: | Lisa Maria Ringel, Márk Somogyvári, Mohammadreza JalaliORCiD, Peter BayerORCiD |
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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): | ![]() |
Externe Anmerkung: | Bibliographieeintrag der Originalveröffentlichung/Quelle |