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An efficient Bayesian inversion of a geothermal prospect using a multivariate adaptive regression spline method

Journal Article · · Applied Energy
 [1];  [2];  [2];  [2];  [2];  [2];  [2]
  1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Sultan Qaboos Univ., Muscat (Oman). Water Research Center
  2. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
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In this study, an efficient Bayesian framework equipped with a multivariate adaptive regression spline (MARS) technique is developed to alleviate computational burdens encountered in a conventional Bayesian inversion of a geothermal prospect. Fast MARS models are developed from training dataset generated by CPU-intensive hydrothermal models and used as surrogate of high-fidelity physical models in Markov Chain Monte Carlo (MCMC) sampling. This Bayesian inference with MARS-enabled MCMC method is used to reduce prior estimates of uncertainty in structural or characteristic hydrothermal flow parameters of the model to posterior distributions. A geothermal prospect near Superstition Mountain in Imperial County of California in USA is used to illustrate the proposed framework and demonstrate the computational efficiency of MARS-based Bayesian inversion. The developed MARS models are also used to efficiently drive calculation of Sobol’ total sensitivity indices. Furthermore, only top sensitive parameters are included in Bayesian inference to further improve the computational efficiency of inversion. Sensitivity analysis also confirms that water circulation through high permeable structures, rather than heat conduction through impermeable granite, is the primary heat transfer method. The presented framework is demonstrated an efficient tool to update knowledge of geothermal prospects by inversing field data. Although only thermal data is used in this study, other type of data, such as flow and transport observations, can be jointly used in this method for underground hydrocarbon reservoirs.

Research Organization:
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Geothermal Technologies Office
Grant/Contract Number:
AC52-07NA27344
OSTI ID:
1809162
Alternate ID(s):
OSTI ID: 1556263
Report Number(s):
LLNL-JRNL--646027; 765970
Journal Information:
Applied Energy, Journal Name: Applied Energy Vol. 136; ISSN 0306-2619
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English

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Related Subjects

15 GEOTHERMAL ENERGY
geothermal prospect
inversion
sensitivity
surrogate
uncertainty