Advancing Subsurface Discovery and Geothermal Monitoring with an Agentic Artificial Intelligence Framework

Abstract

Geothermal field development typically involves complex processes that require multi-disciplinary expertise in each process. Thus, decision-making often demands the integration of geological, geophysical, reservoir engineering, and operational data under tight time constraints. We present Geothermal Analytics and Intelligent Agent, or GAIA, an AI-based system for automation and assistance in geothermal field development. GAIA consists of three core components: GAIA Agent, GAIA Chat, and GAIA Digital Twin, or DT, which together constitute an agentic retrieval-augmented generation (RAG) workflow. Specifically, GAIA Agent, powered by a pre-trained large language model (LLM), designs and manages task pipelines by autonomously querying knowledge bases and orchestrating multi-step analyses. GAIA DT encapsulates classical and surrogate physics models, which, combined with built-in domain-specific subroutines and visualization tools, enable predictive modeling of geothermal systems. Lastly, GAIA Chat serves as a web-based interface for users, featuring a ChatGPT-like layout with additional functionalities such as interactive visualizations, parameter controls, and in-context document retrieval. To ensure GAIA's specialized capability for handling complex geothermal-related tasks, we curate a benchmark test set comprising various geothermal-related scenarios and rigorously evaluate the system's performance. Beyond task-level automation, GAIA is a unified framework that tightly couples physics-based modeling with agentic reasoning, which enables both domain-infused agentic evolutionary algorithm (meta-evolution) and end-to-end geothermal data processing within a single system. This integration highlights GAIA's potential not only as an assistive tool but as a platform for accelerating scientific discovery and enabling more autonomous, data-driven geothermal field development.