Integrating structural geological data into the inverse modelling framework of iTOUGH2

The validity of subsurface flow simulations strongly depends on the accuracy of relevant rock property values and their distribution in space. In realistic simulations, this spatial distribution is based on two geological considerations: (1) the subsurface structural setting, and (2) smaller-scale h...

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Published in:Computers & geosciences Vol. 65; pp. 95 - 109
Main Authors: Wellmann, J. Florian, Finsterle, Stefan, Croucher, Adrian
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.04.2014
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ISSN:0098-3004, 1873-7803
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Abstract The validity of subsurface flow simulations strongly depends on the accuracy of relevant rock property values and their distribution in space. In realistic simulations, this spatial distribution is based on two geological considerations: (1) the subsurface structural setting, and (2) smaller-scale heterogeneity within a hydrostratigraphic unit. Both aspects are subject to uncertainty, whereas techniques to address heterogeneity are well established, no general method exists to evaluate the influence of structural uncertainties. We present a method to include structural geological data (e.g. observations of geological contacts and faults) directly into an inversion framework, with the aim of enabling the inversion routine to adapt a full 3-D geological model with a set of geological parameters. In order to achieve this aim, we use a set of Python modules to combine several pre-existing codes into one workflow, to facilitate the consideration of a structural model in the typical model evaluation steps of sensitivity analysis, parameter estimation, and uncertainty propagation analysis. In a synthetic study, we then test the application of these three steps to analyse CO2 injection into an anticlinal structure with the potential of leakage through a fault zone. We consider several parts of the structural setting as uncertain, most importantly the position of the fault zone. We then evaluate (1) how sensitive CO2 arriving in several observation wells would be with respect to the geological parameters, (2) if it would be possible to determine the leak location from observations in shallow wells, and (3) how parametric uncertainty affects the expected CO2 leakage amount. In all these cases, our main focus is to consider the influence of the primary geological data on model outputs. We demonstrate that the integration of structural data into the iTOUGH2 framework enables the inversion routines to adapt the geological model, i.e. to re-generate the entire structural model based on changes in several sensitive geological parameters. Our workflow is a step towards a combined analysis of uncertainties not only in local heterogeneities but in the structural setting as well, for a more complete integration of geological knowledge into conceptual and numerical models. [Display omitted]
AbstractList The validity of subsurface flow simulations strongly depends on the accuracy of relevant rock property values and their distribution in space. In realistic simulations, this spatial distribution is based on two geological considerations: (1) the subsurface structural setting, and (2) smaller-scale heterogeneity within a hydrostratigraphic unit. Both aspects are subject to uncertainty, whereas techniques to address heterogeneity are well established, no general method exists to evaluate the influence of structural uncertainties. We present a method to include structural geological data (e.g. observations of geological contacts and faults) directly into an inversion framework, with the aim of enabling the inversion routine to adapt a full 3-D geological model with a set of geological parameters. In order to achieve this aim, we use a set of Python modules to combine several pre-existing codes into one workflow, to facilitate the consideration of a structural model in the typical model evaluation steps of sensitivity analysis, parameter estimation, and uncertainty propagation analysis. In a synthetic study, we then test the application of these three steps to analyse CO2 injection into an anticlinal structure with the potential of leakage through a fault zone. We consider several parts of the structural setting as uncertain, most importantly the position of the fault zone. We then evaluate (1) how sensitive CO2 arriving in several observation wells would be with respect to the geological parameters, (2) if it would be possible to determine the leak location from observations in shallow wells, and (3) how parametric uncertainty affects the expected CO2 leakage amount. In all these cases, our main focus is to consider the influence of the primary geological data on model outputs. We demonstrate that the integration of structural data into the iTOUGH2 framework enables the inversion routines to adapt the geological model, i.e. to re-generate the entire structural model based on changes in several sensitive geological parameters. Our workflow is a step towards a combined analysis of uncertainties not only in local heterogeneities but in the structural setting as well, for a more complete integration of geological knowledge into conceptual and numerical models.
The validity of subsurface flow simulations strongly depends on the accuracy of relevant rock property values and their distribution in space. In realistic simulations, this spatial distribution is based on two geological considerations: (1) the subsurface structural setting, and (2) smaller-scale heterogeneity within a hydrostratigraphic unit. Both aspects are subject to uncertainty, whereas techniques to address heterogeneity are well established, no general method exists to evaluate the influence of structural uncertainties. We present a method to include structural geological data (e.g. observations of geological contacts and faults) directly into an inversion framework, with the aim of enabling the inversion routine to adapt a full 3-D geological model with a set of geological parameters. In order to achieve this aim, we use a set of Python modules to combine several pre-existing codes into one workflow, to facilitate the consideration of a structural model in the typical model evaluation steps of sensitivity analysis, parameter estimation, and uncertainty propagation analysis. In a synthetic study, we then test the application of these three steps to analyse CO2 injection into an anticlinal structure with the potential of leakage through a fault zone. We consider several parts of the structural setting as uncertain, most importantly the position of the fault zone. We then evaluate (1) how sensitive CO2 arriving in several observation wells would be with respect to the geological parameters, (2) if it would be possible to determine the leak location from observations in shallow wells, and (3) how parametric uncertainty affects the expected CO2 leakage amount. In all these cases, our main focus is to consider the influence of the primary geological data on model outputs. We demonstrate that the integration of structural data into the iTOUGH2 framework enables the inversion routines to adapt the geological model, i.e. to re-generate the entire structural model based on changes in several sensitive geological parameters. Our workflow is a step towards a combined analysis of uncertainties not only in local heterogeneities but in the structural setting as well, for a more complete integration of geological knowledge into conceptual and numerical models. [Display omitted]
Author Croucher, Adrian
Finsterle, Stefan
Wellmann, J. Florian
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  givenname: J. Florian
  surname: Wellmann
  fullname: Wellmann, J. Florian
  email: florian.wellmann@graduate.uwa.edu.au, jan.florian.wellmann@gmail.com
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  surname: Croucher
  fullname: Croucher, Adrian
  organization: Department of Engineering Science, University of Auckland, Private Bag 92019, Auckland, New Zealand
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Keywords Uncertainty
Parameter estimation
Sensitivity analysis
Monte Carlo
Implicit geological modelling
Multiphase flow simulation
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Snippet The validity of subsurface flow simulations strongly depends on the accuracy of relevant rock property values and their distribution in space. In realistic...
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SubjectTerms carbon dioxide
computer software
computers
Implicit geological modelling
mathematical models
Monte Carlo
Multiphase flow simulation
Parameter estimation
Sensitivity analysis
subsurface flow
Uncertainty
wells
Title Integrating structural geological data into the inverse modelling framework of iTOUGH2
URI https://dx.doi.org/10.1016/j.cageo.2013.10.014
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