EnrgLLM: A Deep Dive into the Development, Training, and Deployment of a Customized Oil and Gas Language Model

Abstract This paper details the creation, testing, and deployment of EnrgLLM (referred to as EnergyLLM in earlier publications), a large language model (LLM) trained in the domain of petroleum engineering. The Society of Petroleum Engineers’ content was acquired and transformed into a question-answer training set and an opensource LLM, Llama 3 from Meta, was fine-tuned on this training set. The result is a language model that can generate higher quality answers about oil and gas questions than other LLMs. To validate this claim, we tested EnrgLLM vs. the base Llama model with subject matter experts. EnrgLLM scored higher than the base model according to their feedback. We then proceeded to prepare the deployment of EnrgLLM for a wider audience and connected it with the OnePetro corpus so it may cite sources in its answers. Each step of this process—the development of the dataset, training, testing, and deployment—are described herein.