摘要
Natural decline in various mainstream oilfield reserves and the high investment capital in upstream exploration and project development have promoted attention towards smaller oilfields referred to as Marginal fields. This provides operators the opportunity to commence exploration and production with minimum requirements of design, installation, and operations. Although the low Capital Expenditure (CAPEX) requirement favors the start-up of marginal oilfield operations, several operators are not able to sustain the field’s operations due to the high Operational Expenditure (OPEX), particularly arising from facilities’ maintenance. The aim of this paper is to review the maintenance strategies adopted in marginal oilfields, assess their effectiveness, and provide a pointer towards efficient and viable maintenance strategies for the sustainability of marginal oilfields. The study showed that time-based preventive maintenance is predominant in the oil industry, which constitutes up to 40% of net operational expenses. In other cases, reactive maintenance is adopted, which often results in an unplanned shutdown, known to be responsible for nearly half of the overall losses of an oil facility. A paradigm shift in maintenance to Reliability Centered Maintenance (RCM) was explored for marginal oilfield, with a comprehensive review of various maintenance strategies, ranging from maintenance optimization strategies, Heuristics and Metaheuristics, Artificial Intelligence (AI), and Data Mining techniques. It was observed that the application of AI best addresses the proposed RCM for marginal oilfields. This was drawn from the recorded limitations of the other concepts from verifiable similar works, where different AI techniques and Data analytics methods have been successfully applied to aid RCM.
Natural decline in various mainstream oilfield reserves and the high investment capital in upstream exploration and project development have promoted attention towards smaller oilfields referred to as Marginal fields. This provides operators the opportunity to commence exploration and production with minimum requirements of design, installation, and operations. Although the low Capital Expenditure (CAPEX) requirement favors the start-up of marginal oilfield operations, several operators are not able to sustain the field’s operations due to the high Operational Expenditure (OPEX), particularly arising from facilities’ maintenance. The aim of this paper is to review the maintenance strategies adopted in marginal oilfields, assess their effectiveness, and provide a pointer towards efficient and viable maintenance strategies for the sustainability of marginal oilfields. The study showed that time-based preventive maintenance is predominant in the oil industry, which constitutes up to 40% of net operational expenses. In other cases, reactive maintenance is adopted, which often results in an unplanned shutdown, known to be responsible for nearly half of the overall losses of an oil facility. A paradigm shift in maintenance to Reliability Centered Maintenance (RCM) was explored for marginal oilfield, with a comprehensive review of various maintenance strategies, ranging from maintenance optimization strategies, Heuristics and Metaheuristics, Artificial Intelligence (AI), and Data Mining techniques. It was observed that the application of AI best addresses the proposed RCM for marginal oilfields. This was drawn from the recorded limitations of the other concepts from verifiable similar works, where different AI techniques and Data analytics methods have been successfully applied to aid RCM.
作者
Olawale D. Adenuga
Ogheneruona E. Diemuodeke
Ayoade O. Kuye
Olawale D. Adenuga;Ogheneruona E. Diemuodeke;Ayoade O. Kuye(Institute of Engineering, Technology, and Innovations Management (METI), University of Port Harcourt, Port Harcourt, Ni-geria;Energy and Thermofluid Research Group, Department of Mechanical Engineering, University of Port Harcourt, Port Har-court, Nigeria;Department of Chemical Engineering, University of Port Harcourt, Port Harcourt, Nigeria)
