Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{180062,
        author = {M. MOHAMED RAFI and J.Arockia Jebson},
        title = {OPTIMIZING OIL RIG OPERATIONS: LEVERAGING SUPPERVISED LEARNING AND EMERGING TECHNOLOGIES FOR ENHANCED EFFICIENCY},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {124-127},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180062},
        abstract = {—The proposed system is an advanced 
platform tailored for oil rig operations, managing the 
lifecycle of oil extraction, processing, and distribution 
through integrated modules for Clients, Extraction, 
Lab, Transport, and Admin. Clients can register, 
manage demands, payments, and track shipments, 
while extraction teams handle extraction details and 
payments. Laboratory personnel ensure quality by 
managing and updating oil tests, and transport teams 
handle logistics and delivery updates. Admins oversee 
the entire operation, ensuring coordination and 
compliance. A machine learning algorithm enhances the 
system by predicting oil demands based on historical 
and external data, and an optimization algorithm 
allocates resources efficiently, dynamically adjusting 
operations to optimize schedules, reduce costs, and 
improve overall efficiency. This integration ensures 
streamlined, cost-effective processes and improved 
client satisfaction within the oil rig industry.},
        keywords = {},
        month = {May},
        }