[4A1-GS-10] AI application:

[4A1-GS-10-01] Application of large language models to rock physics

Evaluation of seismic velocity estimation of CO2 or H2-saturated rocks

〇Shogo Masaya¹ (1. INPEX Corporation)

Keywords:Large language model, Energy transition, Subsurface, Decarbonization

Since the release of ChatGPT, generative AI with a large language model (LLM) has rapidly gained adoption, showing the potential to revolutionize various fields. This study examines a novel rock physics modeling approach, focusing on seismic velocity estimation in rocks saturated with CO2 or H2, using multiple LLM-based algorithms. As subsurface storage of these gases becomes increasingly important for the energy transition and decarbonization, it is crucial to monitor changes in rock elastic properties, such as velocity and density, after gas injection. Although existing theories, empirical relations, and laboratory experiments can estimate these changes, there remains uncertainty in selecting and calibrating the most suitable models. This work investigates whether multiple generic LLMs, trained without domain-specific data, can accurately select models and parameters for four gas-saturated seismic velocity problems. By conducting blind tests with published experimental benchmarks, this study evaluates whether text-based inputs and outputs can yield accurate rock physics estimations, thus offering a new avenue for rapid, data-light modeling in geophysical applications. The results demonstrate that, although the accuracy of responses varies depending on the algorithm and the question, in some cases the models can provide accurate answers within a short time.