@article{KloseGuillemoteauVignolietal.2022,
  author    = {Klose, Tim and Guillemoteau, Julien and Vignoli, Giulio and Tronicke, Jens},
  title     = {Laterally constrained inversion (LCI) of multi-configuration EMI data with tunable sharpness},
  series = {Journal of applied geophysics},
  volume    = {196},
  journal   = {Journal of applied geophysics},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0926-9851},
  doi       = {10.1016/j.jappgeo.2021.104519},
  pages     = {9},
  year      = {2022},
  abstract  = {Frequency-domain electromagnetic (FDEM) data are commonly inverted to characterize subsurface geoelectrical properties using smoothness constraints in 1D inversion schemes assuming a layered medium. Smoothness constraints are suitable for imaging gradual transitions of subsurface geoelectrical properties caused, for example, by varying sand, clay, or fluid content. However, such inversion approaches are limited in characterizing sharp interfaces. Alternative regularizations based on the minimum gradient support (MGS) stabilizers can, instead, be used to promote results with different levels of smoothness/sharpness selected by simply acting on the so-called focusing parameter. The MGS regularization has been implemented for different kinds of geophysical data inversion strategies. However, concerning FDEM data, the MGS regularization has only been implemented for vertically constrained inversion (VCI) approaches but not for laterally constrained inversion (LCI) approaches. We present a novel LCI approach for FDEM data using the MGS regularization for the vertical and lateral direction. Using synthetic and field data examples, we demonstrate that our approach can efficiently and automatically provide a set of model solutions characterized by different levels of sharpness and variable lateral consistencies. In terms of data misfit, the obtained set of solutions contains equivalent models allowing us also to investigate the non-uniqueness of FDEM data inversion.},
  language  = {en}
}
@article{KloseChaparroSchillingetal.2020,
  author    = {Klose, Tim and Chaparro, M. Carme and Schilling, Frank and Butscher, Christoph and Klumbach, Steffen and Blum, Philipp},
  title     = {Fluid flow simulations of a large-scale borehole leakage experiment},
  series = {Transport in Porous Media},
  volume    = {136},
  journal   = {Transport in Porous Media},
  number    = {1},
  publisher = {Springer},
  address   = {New York},
  issn      = {0169-3913},
  doi       = {10.1007/s11242-020-01504-y},
  pages     = {125 -- 145},
  year      = {2020},
  abstract  = {Borehole leakage is a common and complex issue. Understanding the fluid flow characteristics of a cemented area inside a borehole is crucial to monitor and quantify the wellbore integrity as well as to find solutions to minimise existing leakages. In order to improve our understanding of the flow behaviour of cemented boreholes, we investigated experimental data of a large-scale borehole leakage tests by means of numerical modelling using three different conceptual models. The experiment was performed with an autoclave system consisting of two vessels bridged by a cement-filled casing. After a partial bleed-off at the well-head, a sustained casing pressure was observed due to fluid flow through the cementsteel composite. The aim of our simulations is to investigate and quantify the permeability of the cement-steel composite. From our model results, we conclude that the flow occurred along a preferential flow path at the cement-steel interface. Thus, the inner part of the cement core was impermeable during the duration of the experiment. The preferential flow path can be described as a highly permeable and highly porous area with an aperture of about 5 mu m and a permeability of 3 . 10(-12) m(2) (3 Darcy). It follows that the fluid flow characteristics of a cemented area inside a borehole cannot be described using one permeability value for the entire cement-steel composite. Furthermore, it can be concluded that the quality of the cement and the filling process regarding the cement-steel interface is crucial to minimize possible well leakages.},
  language  = {en}
}
@article{KloseGuillemoteauSimonetal.2018,
  author    = {Klose, Tim and Guillemoteau, Julien and Simon, Francois-Xavier and Tronicke, Jens},
  title     = {Toward subsurface magnetic permeability imaging with electromagnetic induction sensors},
  series = {Geophysics},
  volume    = {83},
  journal   = {Geophysics},
  number    = {5},
  publisher = {Society of Exploration Geophysicists},
  address   = {Tulsa},
  issn      = {0016-8033},
  doi       = {10.1190/GEO2017-0827.1},
  pages     = {E335 -- E345},
  year      = {2018},
  abstract  = {In near-surface geophysics, small portable loop-loop electro-magnetic induction (EMI) sensors using harmonic sources with a constant and rather small frequency are increasingly used to investigate the electrical properties of the subsurface. For such sensors, the influence of electrical conductivity and magnetic permeability on the EMI response is well-understood. Typically, data analysis focuses on reconstructing an electrical conductivity model by inverting the out-of-phase response. However, in a variety of near-surface applications, magnetic permeability (or susceptibility) models derived from the in-phase (IP) response may provide important additional information. In view of developing a fast 3D inversion procedure of the IP response for a dense grid of measurement points, we first analyze the 3D sensitivity functions associated with a homogeneous permeable half-space. Then, we compare synthetic data computed using a linear forward-modeling method based on these sensitivity functions with synthetic data computed using full nonlinear forward-modeling methods. The results indicate the correctness and applicability of our linear forward-modeling approach. Furthermore, we determine the advantages of converting IP data into apparent permeability, which, for example, allows us to extend the applicability of the linear forward-modeling method to high-magnetic environments. Finally, we compute synthetic data with the linear theory for a model consisting of a controlled magnetic target and compare the results with field data collected with a four-configuration loop-loop EMI sensor. With this field-scale experiment, we determine that our linear forward-modeling approach can reproduce measured data with sufficiently small error, and, thus, it represents the basis for developing efficient inversion approaches.},
  language  = {en}
}