Optimizing Oil Rig Operations: Leveraging Supervised Learning and Emerging Technologies for Enhanced Efficiency

Authors- Antony Naven Kumar P, Assistant Professor, H. Jayamangala

Abstract-This paper presents an intelligent, integrated platform designed to optimize oil rig operations through the application of supervised machine learning and advanced resource management algorithms. The system streamlines the full lifecycle of oil extraction, processing, quality control, and distribution by coordinating role-specific modules for clients, extraction teams, laboratory personnel, transport units, and administrators. A supervised learning model predicts oil demand by analyzing historical and environmental data, enabling proactive scheduling and operational planning. Complementing this, an optimization algorithm—such as Linear Programming—dynamically allocates resources, minimizing delays and reducing operational costs. The platform further enhances efficiency through real-time communication, centralized data handling, and predictive analytics. Future enhancements, including blockchain integration, AI-based anomaly detection, and mobile application support, are proposed to expand functionality and security. This unified system offers a scalable, cost-effective solution that significantly improves coordination, responsiveness, and overall productivity in oil rig operations.

DOI: /10.61463/ijset.vol.13.issue3.130