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Investigation of Geothermal Potential Zones with Machine Learning in Mainland Portugal
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| 1.63 MB | Adobe PDF |
Autores
Resumo(s)
Earth’s internal heat is explored to produce electricity or used directly in industrial processes or residencies. It is considered to be renewable and cleaner than fossil fuels and has great importance to pursue environmental goals. The exploration phase of geothermal resources is complex and expensive. It requires field surveys, geological, geophysical and geochemical analysis, as well as drilling campaigns. Geospatial data and technologies have been used to target promising sites for further investigations, and helped reduce costs while also pointed to important criteria data related to geothermal potential. Machine learning is a data driven set of technologies that has been successfully used to model environmental parameters, and in the field of geothermal energy it has been used to predict thermal properties of the surface and subsurface. Random Forests and Extreme Gradient Boosting are ensemble machine learning algorithms that have been extensively used in environmental and geological sciences, and have been demonstrated to perform well when predicting thermal properties. This study investigated a methodology that coupled GIS and ML to predict two crucial parameters in geothermal exploration throughout Mainland Portugal: Geothermal gradient and surface Heat flow density. Training data consisted in different types of wells drilled in the study area where the two labels were measured. It was provided by Portugal’s Geology and Energy Laboratory. Features were all publicly available and consisted in geological, hydrogeological, geophysical, weather and terrain data. Data were aggregated in two grids with two spatial resolutions. The results between the two algorithms have been compared and discussed. The most important features that contributed to the models were identified and their relationships with the outputs discussed. The models and the prediction maps over the study area showed the location of zones with higher geothermal gradient and surface heat flow density and can be used to aid geothermal exploration and provide insights for geothermal modelling.
Descrição
Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies
Palavras-chave
Geographical Information Systems Spatial analysis Machine Learning Random Forests Extreme Gradient Boosting Geothermal exploration Geothermal gradient Heat flow