Abstract

Non-productive time (NPT) is a significant challenge in offshore sand control projects, leading to increased operational costs, schedule delays, and reduced efficiency. Factors such as poor equipment logistics, suboptimal workforce allocation, and unpredictable environmental conditions contribute to prolonged downtime. This study presents a data-driven model designed to optimize equipment logistics and workforce management to minimize NPT in offshore sand control operations. The proposed model integrates real-time data analytics, predictive maintenance algorithms, and advanced scheduling techniques to enhance project execution. A machine learning framework is developed to analyze historical project data, identify key downtime contributors, and optimize resource allocation. The model incorporates offshore weather forecasts, equipment failure probabilities, and workforce productivity metrics to develop adaptive schedules that reduce operational inefficiencies. Additionally, a dynamic inventory management system ensures critical equipment availability, preventing delays caused by supply chain disruptions. Field implementation of the data-driven model in offshore sand control projects demonstrated a 25% reduction in NPT, with improvements in logistical coordination and workforce utilization. The integration of predictive maintenance strategies reduced unplanned equipment failures by 30%, while optimized shift scheduling enhanced labor efficiency. The results highlight the potential of data-driven methodologies in mitigating delays and improving the overall success rate of offshore completions. This study contributes to the advancement of offshore sand control strategies by leveraging artificial intelligence (AI) and predictive analytics for enhanced operational planning. The findings emphasize the need for industry adoption of intelligent scheduling and logistics optimization to achieve cost-effective and reliable offshore sand control projects. Future research will focus on refining the model through real-time sensor data integration and further automation of decision-making processes to enhance offshore efficiency.