OPTIMIZING OIL RIG OPERATIONS: LEVERAGING SUPPERVISED LEARNING AND EMERGING TECHNOLOGIES FOR ENHANCED EFFICIENCY

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M. MOHAMED RAFI; J.Arockia Jebson

OPTIMIZING OIL RIG OPERATIONS: LEVERAGING SUPPERVISED LEARNING AND EMERGING TECHNOLOGIES FOR ENHANCED EFFICIENCY

Authors: M. MOHAMED RAFI, J.Arockia Jebson

Unique Paper ID: 180062
Volume: 12
Issue: 1
PageNo: 124-127

Keywords: No Keywords Found

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.

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Cite This Article

ISSN: 2349-6002
Volume: 12
Issue: 1
PageNo: 124-127

OPTIMIZING OIL RIG OPERATIONS: LEVERAGING SUPPERVISED LEARNING AND EMERGING TECHNOLOGIES FOR ENHANCED EFFICIENCY

Available:https://ijirt.org/article?manuscript=180062

27 May, 2025

REVOCABLE CLOUD ASSISTED ATTRIBUTES BASED SIGNCRYPTION USING PHR

23 May, 2025

DEDUPLICATION DYNAMIC PROOF OF STORAGE FOR MULTI USER ENVIRONMENTS

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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