Data-Driven Solutions and Artificial Intelligence in the Energy Sector: Leveraging Open GHGRP Data for Emissions Forecasting for U.S. Petroleum and Natural Gas Assets

Abstract This technical paper explores the application of open-source data from the Environmental Protection Agency's (EPA) Greenhouse Gas Reporting Program (GHGRP) to forecast methane and other greenhouse gas (GHG) emissions across U.S. upstream and downstream assets. It focuses specifically on emissions from onshore and offshore petroleum production operations. By integrating artificial intelligence (AI) techniques, the research aims to develop a robust emissions prediction model to accelerate decarbonization efforts while enhancing sustainability in the energy sector. Classical and Advanced AI algorithms and data analytics approaches were applied to interpret emissions data reported in the GHGRP, covering petroleum and natural gas assets from U.S. upstream sector. The approach included machine learning (ML) techniques such as gradient boosting models and deep learning to capture emission patterns and improve prediction accuracy. Natural Language Processing (NLP) techniques were employed to refine data collection processes, extracting insights from regulatory and operational reports. A comprehensive AI-powered framework was designed to identify key emissions sources and forecast potential reductions, providing actionable intelligence for emissions management strategies across operations. The study successfully leveraged AI models to forecast GHG emissions across petroleum and natural gas assets. The research identified key areas where emissions were concentrated and predicted future trends with high accuracy. Machine learning models, particularly boosting techniques, outperformed traditional statistical approaches, providing superior results in emissions forecasting at both the equipment and system-wide levels. This methodology highlighted significant sources of emissions within this sector and pinpointed where mitigation efforts could yield the greatest impact. Generative AI Large Language Models (LLM) enhanced the capability to process small or large GHG datasets. The hybrid multi-model approach demonstrated a novel use case for processing environmental data, ensuring efficient, accurate analysis across multiple sources. The findings underscore the value of AI in emissions reduction strategies, offering vital insights for energy industry leaders and regulators. The models can be adapted to broader applications, helping the industry reduce its environmental footprint. This study introduces a first-of-its-kind approach that utilizes artificial intelligence (AI) and machine learning (ML) to forecast emissions from U.S. production assets, leveraging open GHGRP data from petroleum and natural gas production facilities. It integrates Generative AI, marking a cutting-edge application and a valuable contribution to the petroleum industry. By establishing a repeatable ML forecasting framework and ensemble of models, this applied research helps the energy industry set an industry baseline for emissions, enabling continuous improvements and precise target setting for emissions efficiency and neutrality. This pioneering effort not only complements existing methodologies for natural gas compression, transmission, and storage but also positions the study at the forefront of industry advancements in achieving carbon emissions management through applications of advanced technologies. The use of Generative AI and LLM-based forecasting enhances energy analytics through sophisticated data extraction and analysis, solidifying its role as a novel and innovative industry-leading exercise.