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Efficient parameter estimation for a methane hydrate model with active subspaces

  • Mario Teixeira ParenteEmail author
  • Steven Mattis
  • Shubhangi Gupta
  • Christian Deusner
  • Barbara Wohlmuth
Original Paper

Abstract

Methane gas hydrates have increasingly become a topic of interest because of their potential as a future energy resource. There are significant economical and environmental risks associated with extraction from hydrate reservoirs, so a variety of multiphysics models have been developed to analyze prospective risks and benefits. These models generally have a large number of empirical parameters which are not known a priori. Traditional optimization-based parameter estimation frameworks may be ill-posed or computationally prohibitive. Bayesian inference methods have increasingly been found effective for estimating parameters in complex geophysical systems. These methods often are not viable in cases of computationally expensive models and high-dimensional parameter spaces. Recently, methods have been developed to effectively reduce the dimension of Bayesian inverse problems by identifying low-dimensional structures that are most informed by data. Active subspaces is one of the most generally applicable methods of performing this dimension reduction. In this paper, Bayesian inference of the parameters of a state-of-the-art mathematical model for methane hydrates based on experimental data from a triaxial compression test with gas hydrate-bearing sand is performed in an efficient way by utilizing active subspaces. Active subspaces are used to identify low-dimensional structure in the parameter space which is exploited by generating a cheap regression-based surrogate model and implementing a modified Markov chain Monte Carlo algorithm. Posterior densities having means that match the experimental data are approximated in a computationally efficient way.

Keywords

Constitutive modeling Soil plasticity Bayesian inversion Dimension reduction 

Mathematics Subject Classification (2010)

62-07 65C20 68U20 

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Notes

Funding information

Financial support for BW, SM, and MTP was provided by the German Research Foundation (DFG, Project WO 671/11-1). The work of SG and CD was further funded by the German Federal Ministries of Economy (BMWi) and Education and Research (BMBF) through the SUGAR project (grant nos. 03SX250, 03SX320A, and 03G0856A), and the EU-FP7 project MIDAS (grant agreement no. 603418).

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Copyright information

© Springer Nature Switzerland AG 2018
corrected publication September/2018

Authors and Affiliations

  1. 1.Chair for Numerical MathematicsTechnical University MunichGarching bei MünchenGermany
  2. 2.Department of MathematicsUniversity of BergenBergenNorway
  3. 3.GEOMAR Helmholtz Centre for Ocean Research KielKielGermany

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