Data-Driven Decision-Making in Drilling Operations: The Intersection of Supply Chain Analytics and Performance Optimization

Michael, Stephen and Hiroshi, Tanaka (2025) Data-Driven Decision-Making in Drilling Operations: The Intersection of Supply Chain Analytics and Performance Optimization. BEST JOURNAL OF INNOVATION IN SCIENCE, RESEARCH AND DEVELOPMENT, 4 (2). pp. 124-129. ISSN 2835-3579

Text 124-129+Data-Driven+Decision-Making+in+Drilling+Operations.pdf Download (635kB)

Abstract

Drilling operations play a crucial role in the energy and resource extraction industries, where efficiency, cost control, and performance optimization are critical to success. The integration of data-driven decision-making through advanced supply chain analytics has emerged as a transformative approach in improving drilling efficiency. This paper explores the intersection of supply chain analytics and drilling performance optimization, highlighting the role of big data, predictive analytics, artificial intelligence (AI), and Internet of Things (IoT) in streamlining drilling processes. It provides insights into the benefits of leveraging analytics for real-time decision-making, risk mitigation, and cost reduction. Through an in-depth analysis, this paper demonstrates how companies can enhance their drilling operations by embracing a data-driven culture that fosters innovation and continuous improvement.

Item Type:	Article
Subjects:	Q Science > Q Science (General)
Divisions:	Postgraduate > Master's of Islamic Education
Depositing User:	Journal Editor
Date Deposited:	28 Feb 2025 12:52
Last Modified:	28 Feb 2025 12:52
URI:	http://eprints.umsida.ac.id/id/eprint/15651

Actions (login required)

View Item