Towart a Smart Supervision Systems in thePetroleum Industry Using ArtificialIntelligence

Abstract

In this study, we aim to enhance the operational efficiency and safety of liquid petroleum storage facilities by integrating advanced analytics and artificial intelligence (AI) for predictive maintenance and real-time monitoring. Liquid petroleum storage facilities are critical nodes in the oil and gas supply chain, and their efficient operation is paramount to ensuring continuous supply and safety [1]. Traditional supervision systems, such as Distributed Control Systems (DCS) and Supervisory Control and Data Acquisition (SCADA), have been widely adopted to monitor and control these facilities [2]. However, these systems can be significantly augmented
with modern AI techniques to predict equipment failures, optimize maintenance schedules, and provide real-time operational insights [3]. This paper builds upon previous research by incorporating advanced machine learning algorithms and real-time data analytics into the existing supervisory framework. We will deploy sensors to collect real-time data on equipment performance, environmental conditions, and operational parameters, which will be integrated into a centralized data platform [4]. Using this data, we will develop and train AI models capable of predicting equipment malfunctions and maintenance needs with high accuracy [5]. The results of this integration are expected to demonstrate a significant reduction in equipment downtime, optimized maintenance schedules, enhanced safety, and overall cost savings [6]. Real-time monitoring and predictive insights will enable more proactive maintenance practices, reducing the risk of unexpected failures and improving operational continuity [7]. This study will provide a robust framework for other petroleum storage facilities to adopt similar enhancements, leveraging AI to drive operational excellence in the industry