Advancing oil and gas emissions assessment through large language model data extraction

Abstract

The oil and gas industry strives to improve environmental stewardship and reduce its carbon footprint, but lacks comprehensive global operational data for accurate environmental assessment and decision-making. This challenge is compounded by dispersed information sources and the high costs of accessing proprietary databases. This paper presents an innovative framework using Large Language Models (LLMs) – specifically GPT-4 and GPT-4o – to extract critical oil and gas asset information from diverse literature sources.

Our framework employs iterative comparisons between GPT-4’s output and a dataset of 129 ground truth documents labeled by domain experts. Through 11 training and testing iterations, we fine-tuned prompts to optimize information extraction. The evaluation process assessed performance using true positive rate, precision, and F1 score metrics. The framework achieved strong results, with a true positive rate of 83.74% and an F1 score of 78.16% on the testing dataset.

The system demonstrated remarkable efficiency, processing 32 documents in 61.41 min with GPT-4o, averaging 7.09 s per extraction - a substantial improvement over the manual method. Cost-effectiveness was also achieved, with GPT-4o reducing extraction costs by a factor of 10 compared to GPT-4.

This research has significant implications for the oil and gas industry. By creating an organized, transparent, and accessible database, we aim to democratize access to critical information. The framework supports more accurate climate modeling efforts, enhances decision-making processes for operations and investments, and contributes to the sector’s ability to meet environmental commitments. These improvements particularly impact emissions reduction and energy transition strategies, potentially transforming how data is extracted and utilized in this field and beyond.