Evaluation of a geothermal prospect using a stochastic joint inversion modeling procedure

A. F B Tompson, R. J. Mellors, A. Ramirez, M. Chen, K. Dyer, X. Yang, J. Wagoner, W. Trainor-Guitton

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

A stochastic inverse algorithm to jointly analyze multiple geophysical and hydrological datasets for a geothermal prospect is presented. The purpose is to improve prospect evaluation and estimate the likelihood of useful temperature and fluid flow fields at depth. The approach combines Bayesian inference with a Markov Chain Monte Carlo (MCMC) global search algorithm to conduct a staged, model-based inversion of the different data sets. Initial estimates of uncertainty in structural or parametric characteristics of the prospect are used to drive large numbers of simulations of hydrothermal fluid flow and related geophysical processes using random realizations of the conceptual geothermal system. The results consist of a subset of these realizations - an equally probable ensemble of alternatives - that best match the observed datasets within a specified norm or tolerance. Statistical (posterior) characteristics of these solutions reflect reductions in the perceived (prior) uncertainties. The method is highly flexible and capable of accommodating multiple and diverse datasets as a means to maximize the utility of all available data to understand system behavior.

Original languageEnglish
Title of host publicationTransactions - Geothermal Resources Council
Pages349-356
Number of pages8
Volume37
EditionPART 1
Publication statusPublished - 2013
EventGeothermal Resources Council Annual Meeting: A Global Resource, from Larderello to Las Vegas, GRC 2013 - Las Vegas, NV, United States
Duration: Sep 29 2013Oct 2 2013

Other

OtherGeothermal Resources Council Annual Meeting: A Global Resource, from Larderello to Las Vegas, GRC 2013
CountryUnited States
CityLas Vegas, NV
Period9/29/1310/2/13

Keywords

  • Exploration
  • Inversion
  • Markov chain Monte Carlo
  • Models
  • Uncertainty quantification

ASJC Scopus subject areas

  • Geophysics
  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment

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  • Cite this

    Tompson, A. F. B., Mellors, R. J., Ramirez, A., Chen, M., Dyer, K., Yang, X., Wagoner, J., & Trainor-Guitton, W. (2013). Evaluation of a geothermal prospect using a stochastic joint inversion modeling procedure. In Transactions - Geothermal Resources Council (PART 1 ed., Vol. 37, pp. 349-356)