A Python-based stochastic library for assessing geothermal power potential using the volumetric method in a liquid-dominated reservoir

Carlos Pocasangre, Yasuhiro Fujimitsu

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

We present a Python-based stochastic library for assessing geothermal power potential using the volumetric method in a liquid-dominated reservoir. The specific aims of this study are to use the volumetric method, “heat in place,” to estimate electrical energy production ability from a geothermal liquid-dominated reservoir, and to build a Python-based stochastic library with useful methods for running such simulations. Although licensed software is available, we selected the open-source programming language Python for this task. The Geothermal Power Potential Evaluation stochastic library (GPPeval) is structured as three essential objects including a geothermal power plant module, a Monte Carlo simulation module, and a tools module. In this study, we use hot spring data from the municipality of Nombre de Jesus, El Salvador, to demonstrate how the GPPeval can be used to assess geothermal power potential. Frequency distribution results from the stochastic simulation shows that this area could initially support a 9.16-MWe power plant for 25 years, with a possible expansion to 17.1 MWe. Further investigations into the geothermal power potential will be conducted to validate the new data.

Original languageEnglish
Pages (from-to)164-176
Number of pages13
JournalGeothermics
Volume76
DOIs
Publication statusPublished - Nov 1 2018

All Science Journal Classification (ASJC) codes

  • Renewable Energy, Sustainability and the Environment
  • Geotechnical Engineering and Engineering Geology
  • Geology

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