THE INTEGRATION OF ARTIFICIAL INTELLIGENCE IN THE OIL AND GAS PROJECT MANAGEMENT SECTOR
Abstract: The integration of Artificial Intelligence (AI) into the oil and gas project management sector is rapidly transforming traditional practices, offering a range of benefits from enhanced decision-making to improved efficiency and knowledge sharing. This paper explores the pros and cons of AI in this sector, focusing on how AI technologies such as fully automated verification, on-spot quantification tools, live chat interactions, and dynamic project scheduling can revolutionize project management. AI enhances decision-making by providing real-time data analysis and predictive analytics, and it significantly improves the knowledge base of engineers and stakeholders through instant access to information and real-time data. A comparative analysis between conventional and AI-powered review methods highlights the increased efficiency, accuracy, and reliability offered by AI. The paper concludes with a discussion on the future outlook of AI in the oil and gas project management sector, emphasizing ongoing advancements and the potential for broader adoption. Keywords: Artificial Intelligence (AI), gas project management sector, transforming traditional practices, AI technologies. Title: THE INTEGRATION OF ARTIFICIAL INTELLIGENCE IN THE OIL AND GAS PROJECT MANAGEMENT SECTOR Author: Ali F. AlShehab, Mustafa S. AlSaigh International Journal of Management and Commerce Innovations ISSN 2348-7585 (Online) Vol. 12, Issue 1, April 2024 - September 2024 Page No: 196-199 Research Publish Journals Website: www.researchpublish.com Published Date: 21-August-2024 Amendment Date: 29-August-2024 DOI: https://doi.org/10.5281/zenodo.13463060 Paper Download Link (Source) https://www.researchpublish.com/papers/the-integration-of-artificial-intelligence-in-the-oil-and-gas-project-management-sector
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2024-08-21
- DOI
- https://doi.org/10.5281/zenodo.13355389
- Primary Topic
- Reservoir Engineering and Simulation Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00