Deep-Neural-Network-based Process Data Simulation Model for Production Well of a Geothermal Power Plant

Atsuhiro Imagawa*, Akira Yoshida, Yoshiharu Amano

*Corresponding author for this work

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

Abstract

Some production wells of geothermal power plants sometimes shut down unintentionally. Before the unintentional shutdown, unstable fluctuations in pressure and flow rate at the wellhead are observed. Because of the difficulties in observing the ground, it is constrained to predict the performance of the steam at the wellhead. If the abrupt shutdown symptoms can be detected and the shutdown prevented, then the power generation continuation can be achieved. The authors have proposed a method for detecting anomalies using 1-Dimensional convolutional neural networks (1D-CNN) to predict this phenomenon. There is an empirical rule that opening the bypass valve can prevent a sudden pressure drop. It is necessary to conduct a comparative experiment between cases relevant to valve operation. This comparative experiment is difficult to conduct because the condition of the well differs from case to case, and therefore the authors could not measure it. Thus, the authors proposed another 1D-CNN model to predict the pressure at the wellhead. It was verified that this model steadily predicts the pressure performance several hours in advance. Consequently, the authors can virtually compare the past measured data with the predicted data to investigate the validity of the valve operation.

Original languageEnglish
Title of host publicationECOS 2021 - 34th International Conference on Efficency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems
PublisherECOS 2021 Program Organizer
Pages531-542
Number of pages12
ISBN (Electronic)9781713843986
Publication statusPublished - 2021
Event34th International Conference on Efficency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, ECOS 2021 - Taormina, Sicily, Italy
Duration: 2021 Jun 282021 Jul 2

Publication series

NameECOS 2021 - 34th International Conference on Efficency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems

Conference

Conference34th International Conference on Efficency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, ECOS 2021
Country/TerritoryItaly
CityTaormina, Sicily
Period21/6/2821/7/2

Keywords

  • Convolutional Neural Network
  • Geothermal Power Plant
  • Timeseries Prediction

ASJC Scopus subject areas

  • Energy(all)
  • Engineering(all)
  • Environmental Science(all)

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