This journal examines the integration of AI-enhanced Remotely Operated Vehicle (ROV) systems and Internet of Things (IoT) protocols in subsea infrastructure management for the oil and gas industry. Utilizing deep learning algorithms such as Convolutional Neural Networks (CNNs) for real-time image analysis and anomaly detection, and machine learning models like Support Vector Machines (SVMs) and Random Forests for predictive maintenance, the system processes large volumes of multi-sensor data to assess the structural health of subsea assets. These AI-driven ROVs—including work-class models like the Saab Seaeye Leopard and Oceaneering Millennium Plus—are deployed alongside IoT-enabled sensors communicating via MQTT and LoRaWAN protocols to enable continuous remote monitoring and automated decision-making. Field deployments have demonstrated measurable improvements: a 30% reduction in unplanned downtime, a 25% decrease in maintenance costs, and a 20% increase in equipment reliability. Additionally, remote monitoring has led to a 50% reduction in hazardous human interventions. These results underscore the transformative potential of AI and IoT technologies in enhancing operational safety, optimizing maintenance, and reducing the environmental impact of deepwater oil and gas operations.
Cite this paper
Ojuekaiye, O. (2025). Industry 4.0 Remotely Operated Vehicle Management for Safer, Greener Oil and Gas Operations. Open Access Library Journal, 12, e3398. doi: http://dx.doi.org/10.4236/oalib.1113398.
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