Enhanced Oil Recovery (EOR) Techniques and the Role of AI Technology in the Nigerian Oil and Gas Industry

Abstract

Enhanced Oil Recovery (EOR) techniques have become a cornerstone in the oil and gas industry, playing a pivotal role in extending the productive life of oil reservoirs and maximizing the extraction of hydrocarbons. Traditional recovery methods are often limited in their ability to fully exploit oil reserves, recovering only a fraction of the total available hydrocarbons. As oil fields age and global reserves of easily accessible oil become increasingly depleted, the adoption of advanced EOR methods has become essential to meet the ever-growing global energy demands. EOR offers the potential to recover additional oil that would otherwise remain trapped in reservoirs, thereby ensuring the sustainability and profitability of oil production in the long term. The advent of Artificial Intelligence (AI) technologies has introduced a new dimension to EOR by enabling more efficient, cost-effective, and precise operations. AI, with its capabilities for machine learning, predictive analytics, and data-driven decision-making, can transform how oil fields are managed, especially in terms of optimizing recovery rates and reducing operational uncertainties. Through the use of AI, operators can process and analyze vast amounts of data from reservoirs in real-time, adjust recovery strategies dynamically, and minimize the risks associated with traditional EOR methods. The integration of AI into EOR not only enhances recovery but also improves the accuracy of forecasts, reduces downtime, and allows for better resource allocation, leading to substantial cost savings. This paper explores the various EOR techniques currently employed in the oil and gas industry, including thermal methods, gas injection, and chemical EOR, highlighting their individual strengths and limitations. The focus then shifts to the application of EOR in the Nigerian oil and gas market, a sector that faces unique challenges due to the aging of many of its oil fields and the technical and financial barriers to adopting advanced recovery methods. Nigeria, as one of the largest oil producers in Africa, has a vested interest in extending the life of its mature oil fields, and Port Harcourt, the hub of oil and gas activities in the country, represents a crucial case study for the implementation of cutting-edge EOR techniques. The paper presents a case study based in Port Harcourt, Nigeria, where AI-driven EOR solutions were applied to optimize gas injection processes and improve oil recovery in mature reservoirs. The case study offers insights into how AI was employed to analyze real-time data, adjust gas injection patterns, and provide more accurate production forecasts, resulting in enhanced recovery rates and reduced operational costs. The study also underscores the economic and operational benefits that AI can bring to the oil and gas sector, particularly in regions with aging infrastructure and limited resources. Through an extensive literature review, detailed methodology, and the in-depth analysis of the Port Harcourt case study, this research aims to demonstrate the transformative potential of AI in EOR operations. By exploring both the technical and economic impacts of integrating AI into EOR processes, the paper highlights how AI can serve as a critical tool in ensuring the long-term viability of oil production in Nigeria and beyond. The research also addresses the broader implications of AI-driven EOR technologies for global oil markets, particularly in terms of sustainability, efficiency, and cost reduction. The paper emphasizes the importance of adopting AI-enhanced EOR technologies in the oil and gas industry to maximize hydrocarbon recovery and extend the life of oil reservoirs, particularly in mature fields. The integration of AI not only enhances the technical capabilities of EOR but also provides a viable pathway to overcoming many of the challenges that the oil and gas industry faces today, ensuring the continued supply of energy to meet global demand while reducing environmental impact and operational costs.