Efficient Modeling, Optimization, and LLM-Assisted Decision Support for Geothermal Well Arrays

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introduced AI tool, NotebookLM, to accelerate the generation of quantitative geothermal benchmarks with only new unpublished questions.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.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2025

Author dc:creator

• Ouko, Edwin O.

Advisor dc:contributor.advisor

• Edelman, Alan

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/162974

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/162974