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@PHDTHESIS{Vogt:211527,
      author       = {Vogt, Christian},
      othercontributors = {Clauser, Christoph},
      title        = {{O}ptimization of geothermal energy reservoir modeling
                      using advanced numerical tools for stochastic parameter
                      estimation and quantifying uncertainties},
      volume       = {13},
      address      = {Aachen},
      publisher    = {E.ON Energy Research Center, RWTH Aachen University},
      reportid     = {RWTH-CONV-143697},
      series       = {E.On Energy Research Center : GGE - Applied geophysics and
                      geothermal energy},
      pages        = {III, 145 S. : Ill., graph. Darst., Kt.},
      year         = {2013},
      note         = {Zsfassung in dt. und engl. Sprache; Zugl.: Aachen, Techn.
                      Hochsch., Diss., 2013},
      abstract     = {Geothermal energy is an option for low carbon production of
                      heat or electric energy. For further developments of this
                      resource, a major obstacle is the risk of project failure
                      due to uncertain estimates of flow rate and temperature
                      (and, hence, produced power) of geothermal installations. In
                      this work, I develop and apply stochastic methods and
                      modeling strategies for predicting the variation of
                      pressure, temperature, and their uncertainty with time
                      within geothermal reservoirs based on observed thermal and
                      hydraulic rock property distributions. This comprises
                      stochastic forward and inverse modeling approaches for
                      simulating heat and tracer transport as well as fluid flow
                      numerically. The approaches reduce the corresponding a
                      priori uncertainties of perturbed parameters and states
                      drastically by $50\%-67\%$ in case of temperature at a depth
                      of 2000 m, depending on the target location. Furthermore, I
                      estimate the spatial distribution of permeability as well as
                      its uncertainty by applying the stochastic assimilation
                      technique of Ensemble Kalman Filtering on production data
                      for sedimentary rocks and fractured hard rocks. This
                      addresses structure and parameter heterogeneity within the
                      reservoir. I study different geothermal reservoirs, such as
                      (i) numerous synthetic reservoirs to test the tools of
                      Sequential Gaussian Simulation combined with geostatistical
                      post-processing and Ensemble Kalman Filter. (ii) Further, I
                      quantify temperature uncertainties of a doublet system in a
                      sedimentary reservoir in The Hague, The Netherlands. (iii)
                      In addition to temperature uncertainties, I study pressure
                      uncertainties at a reservoir in the north-eastern German
                      basin. Here, also a single-well design for exploitation of
                      geothermal energy along a fault zone proofs to represent an
                      alternative to doublet layouts. By gradient-based
                      deterministic Bayesian inversion, basal specific heat flow
                      is revealed. (iv) Finally, I investigate the hard rock
                      reservoir of the Enhanced Geothermal System at
                      Soultz-sous-Forêts, France, using Sequential Gaussian
                      Simulation and Ensemble Kalman Filtering in an equivalent
                      porous medium approach. A tracer circulation test performed
                      in 2005 provides data for the inversion. Applying the two
                      different stochastic methods allows for identifying best
                      estimates for the heterogeneously distributes hydraulic
                      parameters, studying their non-uniqueness, and comparing the
                      results from stochastic (massive Monte Carlo, Ensemble
                      Kalman Filter) and deterministic (gradient-based Bayesian
                      inversion) estimation techniques. Based on the Ensemble
                      Kalman Filter estimation results, I perform a long-term
                      performance prediction with regard to transient temperature
                      variation including corresponding uncertainties. The
                      presented work flows constitute a method for creating
                      calibrated reservoir models based on data which will allow
                      the operators of a geothermal installation to compute
                      production scenarios optimized with respect to profit or
                      sustainability.},
      keywords     = {Geothermische Energie (SWD) / Unsicherheit (SWD) /
                      Modellierung (SWD) / Kalman-Filter (SWD)},
      cin          = {616400 / 530000 / 532610},
      ddc          = {550},
      cid          = {$I:(DE-82)616400_20140620$ / $I:(DE-82)530000_20140620$ /
                      $I:(DE-82)532610_20140620$},
      typ          = {PUB:(DE-HGF)11 / PUB:(DE-HGF)3},
      urn          = {urn:nbn:de:hbz:82-opus-45088},
      url          = {https://publications.rwth-aachen.de/record/211527},
}