Evaluating Financial Risks of Carbon Capture in the Oil & Gas Industry Using Advanced Machine Learning Techniques

Abstract

This paper thoroughly examines the financial risks and opportunities related to implementing carbon capture and storage (CCS) in the oil and gas sector.We utilize sophisticated machine learning techniques to create a robust methodology for measuring CCS projects’ economic feasibility and risk level. Our approach encompasses various crucial factors, such as investment costs, operational expenditures, carbon pricing, and the oil and gas market dynamics.By utilizing discounted cash flow modeling, sensitivity analysis, and Monte Carlo simulation, we produce probabilistic financial results that offer valuable insights for decision-makers. The findings emphasize the crucial significance of policy backing, technological progress, and strategic portfolio management in influencing the financial outcomes of CCS investments. This study enhances the comprehension of Carbon Capture and Storage (CCS) as an essential instrument for reducing carbon emissions in the oil and gas industry while effectively managing the intricate financial aspects of transitioning to cleaner energy sources.