Predicting real-time geothermal well flow rate and enthalpy with machine learning techniques
Geothermal energy is a sustainable energy source offering reliable and renewable energy solutions. However, accurately measuring geothermal well output like flow rate and enthalpy for wells that produce a two-phase fluid remains challenging due to the complexity and infrequency of traditional method...
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Format: | Master Thesis |
Language: | English |
Published: |
2024
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Online Access: | https://hdl.handle.net/1946/48691 |
_version_ | 1821555758574075904 |
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author | Agata Rostran Largaespada 1990- |
author2 | Háskólinn í Reykjavík |
author_facet | Agata Rostran Largaespada 1990- |
author_sort | Agata Rostran Largaespada 1990- |
collection | Skemman (Iceland) |
description | Geothermal energy is a sustainable energy source offering reliable and renewable energy solutions. However, accurately measuring geothermal well output like flow rate and enthalpy for wells that produce a two-phase fluid remains challenging due to the complexity and infrequency of traditional methods. This thesis addresses these issues by continuing the work of developing a real-time method to measure flow rate and enthalpy from geothermal wells without interrupting operations. The focus is on accurately estimating geothermal fluids' flow rate and enthalpy using advanced rule-based models and machine learning techniques. This research integrates data-driven approaches for continuous monitoring and early detection of well performance changes by using measurements from Landsvirkjun's geothermal operations conducted in 2019, 2020, 2021, and 2023. The study employs a specialized differential pressure orifice plate meter setup at Theistareykir and Bjarnarflag Geothermal Power Plants, providing detailed measurements critical for the models. The most effective model employed Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for noise reduction, Recursive Feature Elimination with Cross-Validation (RFECV) for precise feature selection, and Random Forest Regression (RFR) with five key features, achieving a Root Mean Square Error (RMSE) of 0.011. This approach can significantly enhance the efficiency and accuracy of geothermal power production measurements, offering insights into real-time monitoring and operational optimization GRÓ Geothermal Training Programme, Iceland Nicaraguan Electricity Company, Nicaragua |
format | Master Thesis |
genre | Iceland |
genre_facet | Iceland |
geographic | Bjarnarflag |
geographic_facet | Bjarnarflag |
id | ftskemman:oai:skemman.is:1946/48691 |
institution | Open Polar |
language | English |
long_lat | ENVELOPE(-16.867,-16.867,65.633,65.633) |
op_collection_id | ftskemman |
op_relation | https://hdl.handle.net/1946/48691 |
publishDate | 2024 |
record_format | openpolar |
spelling | ftskemman:oai:skemman.is:1946/48691 2025-01-16T22:39:03+00:00 Predicting real-time geothermal well flow rate and enthalpy with machine learning techniques Agata Rostran Largaespada 1990- Háskólinn í Reykjavík 2024-08 application/pdf https://hdl.handle.net/1946/48691 en eng https://hdl.handle.net/1946/48691 Orkuverkfræði Sustainable energy engineering Meistaraprófsritgerðir Jarðhitavinnsla Rennslismælingar Geothermal power plants Geothermal resources Flow meters Thesis Master's 2024 ftskemman 2024-10-23T00:02:50Z Geothermal energy is a sustainable energy source offering reliable and renewable energy solutions. However, accurately measuring geothermal well output like flow rate and enthalpy for wells that produce a two-phase fluid remains challenging due to the complexity and infrequency of traditional methods. This thesis addresses these issues by continuing the work of developing a real-time method to measure flow rate and enthalpy from geothermal wells without interrupting operations. The focus is on accurately estimating geothermal fluids' flow rate and enthalpy using advanced rule-based models and machine learning techniques. This research integrates data-driven approaches for continuous monitoring and early detection of well performance changes by using measurements from Landsvirkjun's geothermal operations conducted in 2019, 2020, 2021, and 2023. The study employs a specialized differential pressure orifice plate meter setup at Theistareykir and Bjarnarflag Geothermal Power Plants, providing detailed measurements critical for the models. The most effective model employed Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for noise reduction, Recursive Feature Elimination with Cross-Validation (RFECV) for precise feature selection, and Random Forest Regression (RFR) with five key features, achieving a Root Mean Square Error (RMSE) of 0.011. This approach can significantly enhance the efficiency and accuracy of geothermal power production measurements, offering insights into real-time monitoring and operational optimization GRÓ Geothermal Training Programme, Iceland Nicaraguan Electricity Company, Nicaragua Master Thesis Iceland Skemman (Iceland) Bjarnarflag ENVELOPE(-16.867,-16.867,65.633,65.633) |
spellingShingle | Orkuverkfræði Sustainable energy engineering Meistaraprófsritgerðir Jarðhitavinnsla Rennslismælingar Geothermal power plants Geothermal resources Flow meters Agata Rostran Largaespada 1990- Predicting real-time geothermal well flow rate and enthalpy with machine learning techniques |
title | Predicting real-time geothermal well flow rate and enthalpy with machine learning techniques |
title_full | Predicting real-time geothermal well flow rate and enthalpy with machine learning techniques |
title_fullStr | Predicting real-time geothermal well flow rate and enthalpy with machine learning techniques |
title_full_unstemmed | Predicting real-time geothermal well flow rate and enthalpy with machine learning techniques |
title_short | Predicting real-time geothermal well flow rate and enthalpy with machine learning techniques |
title_sort | predicting real-time geothermal well flow rate and enthalpy with machine learning techniques |
topic | Orkuverkfræði Sustainable energy engineering Meistaraprófsritgerðir Jarðhitavinnsla Rennslismælingar Geothermal power plants Geothermal resources Flow meters |
topic_facet | Orkuverkfræði Sustainable energy engineering Meistaraprófsritgerðir Jarðhitavinnsla Rennslismælingar Geothermal power plants Geothermal resources Flow meters |
url | https://hdl.handle.net/1946/48691 |