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 |
|---|---|
| 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 |
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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
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.
Transactions - Geothermal Resources Council. Vol. 37 PART 1. ed. 2013. p. 349-356.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
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
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M3 - Conference contribution
AN - SCOPUS:84897593688
SN - 9781629932859
VL - 37
SP - 349
EP - 356
BT - Transactions - Geothermal Resources Council
ER -