{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/162974"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/162974","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Efficient Modeling, Optimization, and LLM-Assisted Decision Support for Geothermal Well Arrays","abstract":"Geothermal well arrays, which organize multiple geothermal wells into carefully planned geometric configurations, provide an opportunity to enhance energy production capacity and increase fault tolerance of geothermal systems. Closed-loop geothermal systems (CLGS), a type of geothermal well design, promises to allow harnessing of geothermal energy in any location with minimal adverse environmental impact. I demonstrate how the development of these emerging geothermal technologies could be accelerated by recent advances in large language models (LLMs) in conjunction with high-level high-performance programming languages like Julia. In particular, I focus on how LLMs could be used in design brainstorming and to increase efficiency in numerical modeling. I assess the potential of state-of-the-art LLMs such as ChatGPT, Gemini, Claude, Grok, and a domain-specific model, AskGDR, as expert assistants in geothermal research. Owing to the unpredictable reliability of LLMs, there is a constant need for objective evaluation benchmarks in various domains. I propose a novel approach, leveraging Google’s recently introduced AI tool, NotebookLM, to accelerate the generation of quantitative geothermal benchmarks with only new unpublished questions. In addition, I propose the use of blackbox optimization as a computationally less costly alternative to approximate the optimal configuration of CLGS wells in a geothermal array to minimize thermal interference and improve heat energy production. I evaluate several optimization strategies such as Bayesian optimization, particle swarm optimization, natural evolution strategies, differential evolution optimization, Nelder-Mead, and simulated annealing on various performance characteristics such as convergence speed and highest production capacity attained.","abstract_html":"Geothermal well arrays, which organize multiple geothermal wells into carefully planned geometric configurations, provide an opportunity to enhance energy production capacity and increase fault tolerance of geothermal systems. Closed-loop geothermal systems (CLGS), a type of geothermal well design, promises to allow harnessing of geothermal energy in any location with minimal adverse environmental impact. I demonstrate how the development of these emerging geothermal technologies could be accelerated by recent advances in large language models (LLMs) in conjunction with high-level high-performance programming languages like Julia. In particular, I focus on how LLMs could be used in design brainstorming and to increase efficiency in numerical modeling. I assess the potential of state-of-the-art LLMs such as ChatGPT, Gemini, Claude, Grok, and a domain-specific model, AskGDR, as expert assistants in geothermal research. Owing to the unpredictable reliability of LLMs, there is a constant need for objective evaluation benchmarks in various domains. I propose a novel approach, leveraging Google’s recently introduced AI tool, NotebookLM, to accelerate the generation of quantitative geothermal benchmarks with only new unpublished questions. In addition, I propose the use of blackbox optimization as a computationally less costly alternative to approximate the optimal configuration of CLGS wells in a geothermal array to minimize thermal interference and improve heat energy production. I evaluate several optimization strategies such as Bayesian optimization, particle swarm optimization, natural evolution strategies, differential evolution optimization, Nelder-Mead, and simulated annealing on various performance characteristics such as convergence speed and highest production capacity attained.","abstract_has_math":false,"creators":["Ouko, Edwin O."],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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Closed-loop geothermal systems (CLGS), a type of geothermal well design, promises to allow harnessing of geothermal energy in any location with minimal adverse environmental impact. I demonstrate how the development of these emerging geothermal technologies could be accelerated by recent advances in large language models (LLMs) in conjunction with high-level high-performance programming languages like Julia. In particular, I focus on how LLMs could be used in design brainstorming and to increase efficiency in numerical modeling. I assess the potential of state-of-the-art LLMs such as ChatGPT, Gemini, Claude, Grok, and a domain-specific model, AskGDR, as expert assistants in geothermal research. Owing to the unpredictable reliability of LLMs, there is a constant need for objective evaluation benchmarks in various domains. I propose a novel approach, leveraging Google’s recently introduced AI tool, NotebookLM, to accelerate the generation of quantitative geothermal benchmarks with only new unpublished questions. In addition, I propose the use of blackbox optimization as a computationally less costly alternative to approximate the optimal configuration of CLGS wells in a geothermal array to minimize thermal interference and improve heat energy production. I evaluate several optimization strategies such as Bayesian optimization, particle swarm optimization, natural evolution strategies, differential evolution optimization, Nelder-Mead, and simulated annealing on various performance characteristics such as convergence speed and highest production capacity attained."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.Eng."]},{"key":"dc:title","label":"Title","values":["Efficient Modeling, Optimization, and LLM-Assisted Decision Support for Geothermal Well Arrays"]}]}],"canonical_facts":{"dc:contributor.advisor":["Edelman, Alan"],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science"],"dc:creator":["Ouko, Edwin O."],"dc:date.accessioned":["2025-10-06T17:37:40Z"],"dc:date.available":["2025-10-06T17:37:40Z"],"dc:date.issued":["2025-05"],"dc:description.abstract":["Geothermal well arrays, which organize multiple geothermal wells into carefully planned geometric configurations, provide an opportunity to enhance energy production capacity and increase fault tolerance of geothermal systems. Closed-loop geothermal systems (CLGS), a type of geothermal well design, promises to allow harnessing of geothermal energy in any location with minimal adverse environmental impact. I demonstrate how the development of these emerging geothermal technologies could be accelerated by recent advances in large language models (LLMs) in conjunction with high-level high-performance programming languages like Julia. In particular, I focus on how LLMs could be used in design brainstorming and to increase efficiency in numerical modeling. I assess the potential of state-of-the-art LLMs such as ChatGPT, Gemini, Claude, Grok, and a domain-specific model, AskGDR, as expert assistants in geothermal research. Owing to the unpredictable reliability of LLMs, there is a constant need for objective evaluation benchmarks in various domains. I propose a novel approach, leveraging Google’s recently introduced AI tool, NotebookLM, to accelerate the generation of quantitative geothermal benchmarks with only new unpublished questions. In addition, I propose the use of blackbox optimization as a computationally less costly alternative to approximate the optimal configuration of CLGS wells in a geothermal array to minimize thermal interference and improve heat energy production. 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