Bayesian and Neural Network Approaches to Estimate Deep Temperature Distribution for Assessing a Supercritical Geothermal System: Evaluation Using a Numerical Model

Kazuya Ishitsuka, Yosuke Kobayashi, Norihiro Watanabe, Yusuke Yamaya, Elvar Bjarkason, Anna Suzuki, Toru Mogi, Hiroshi Asanuma, Tatsuya Kajiwara, Takeshi Sugimoto, Ryoichi Saito

Research output: Contribution to journalArticlepeer-review

Abstract

The temperature distribution at depth is a key variable when assessing the potential of a supercritical geothermal resource as well as a conventional geothermal resource. Data-driven estimation by a machine-learning approach is a promising way to estimate temperature distributions at depth in geothermal fields. In this study, we developed two methodologies—one based on Bayesian estimation and the other on neural networks—to estimate temperature distributions in geothermal fields. These methodologies can be used to supplement existing temperature logs, by estimating temperature distributions in unexplored regions of the subsurface, based on electrical resistivity data, observed geological/mineralogical boundaries, and microseismic observations. We evaluated the accuracy and characteristics of these methodologies using a numerical model of the Kakkonda geothermal field, Japan, where a temperature above 500 °C was observed below a depth of about 3.7 km. When using geological and geophysical knowledge as prior information for the machine learning methods, the results demonstrate that the approaches can provide subsurface temperature estimates that are consistent with the temperature distribution given by the numerical model. Using a numerical model as a benchmark helps to understand the characteristics of the machine learning approaches and may help to identify ways of improving these methods.

Original languageEnglish
Pages (from-to)3289-3314
Number of pages26
JournalNatural Resources Research
Volume30
Issue number5
DOIs
Publication statusPublished - 2021 Oct

Keywords

  • Bayesian estimation
  • Data-driven discovery
  • Kakkonda geothermal field
  • Neural networks
  • Supercritical geothermal system
  • Temperature estimation

ASJC Scopus subject areas

  • Environmental Science(all)

Fingerprint

Dive into the research topics of 'Bayesian and Neural Network Approaches to Estimate Deep Temperature Distribution for Assessing a Supercritical Geothermal System: Evaluation Using a Numerical Model'. Together they form a unique fingerprint.

Cite this