### 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 language | English |
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Title of host publication | Transactions - Geothermal Resources Council |

Pages | 349-356 |

Number of pages | 8 |

Volume | 37 |

Edition | PART 1 |

Publication status | Published - 2013 |

Event | Geothermal Resources Council Annual Meeting: A Global Resource, from Larderello to Las Vegas, GRC 2013 - Las Vegas, NV, United States Duration: Sep 29 2013 → Oct 2 2013 |

### Other

Other | Geothermal Resources Council Annual Meeting: A Global Resource, from Larderello to Las Vegas, GRC 2013 |
---|---|

Country | United States |

City | Las Vegas, NV |

Period | 9/29/13 → 10/2/13 |

### Fingerprint

### 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

### Cite this

*Transactions - Geothermal Resources Council*(PART 1 ed., Vol. 37, pp. 349-356)

**Evaluation of a geothermal prospect using a stochastic joint inversion modeling procedure.** / Tompson, A. F B; Mellors, R. J.; Ramirez, A.; Chen, M.; Dyer, K.; Yang, X.; Wagoner, J.; Trainor-Guitton, W.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Transactions - Geothermal Resources Council.*PART 1 edn, vol. 37, pp. 349-356, Geothermal Resources Council Annual Meeting: A Global Resource, from Larderello to Las Vegas, GRC 2013, Las Vegas, NV, United States, 9/29/13.

}

TY - GEN

T1 - Evaluation of a geothermal prospect using a stochastic joint inversion modeling procedure

AU - Tompson, A. F B

AU - Mellors, R. J.

AU - Ramirez, A.

AU - Chen, M.

AU - Dyer, K.

AU - Yang, X.

AU - Wagoner, J.

AU - Trainor-Guitton, W.

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

KW - Exploration

KW - Inversion

KW - Markov chain Monte Carlo

KW - Models

KW - Uncertainty quantification

UR - http://www.scopus.com/inward/record.url?scp=84897593688&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84897593688&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84897593688

SN - 9781629932859

VL - 37

SP - 349

EP - 356

BT - Transactions - Geothermal Resources Council

ER -