QR Code Link : Type : Thesis Subject : Social Sciences, International Culture, Corporate Social Policy Main Author : Azrool Nizam Bin Azman Title : The Impact of AI-Driven Predictive Analytics on Project Risk Management: A Study on Oil and Gas Exploration and Well Testing Content Type : still image (rdacontent) Media Type : computer (rdamedia) Carrier Type : online resource (rdacarrier)

Place of Production : Kuala Lumpur Publisher : Tun Razak Graduate School Year of Publication : 2024 Notes : Research Project Paper Submitted in Partial Fulfilment of the Requirements \r\nfor the Degree of Master of Business Administration\r\nUniversiti Tun Abdul Razak\r\nOctober 2024 Corporate Name : UNIRAZAK Library PDF Full Text : Login required to access this item.

Abstract : UNIRAZAK Library

This study investigates the role of predictive analytics in managing project risks within the oil and gas industry, particularly focusing on exploration and well testing activities. The sector faces numerous risks, including equipment malfunctions, operational delays, and safety hazards, which can result in financial losses and environmental damage. Traditional risk management approaches often rely on historical data and expert opinions, which may not be sufficient for predicting new risks. Predictive analytics, utilizing large datasets and advanced algorithms, offers a more effective way to enhance risk prediction, improve efficiency, and support better decision-making. Industry professionals were surveyed to assess the current use of predictive analytics and its perceived advantages and challenges. The findings indicate that AI predictive analytics can significantly enhance risk management in the oil and gas sector, offering real-time insights and enabling more proactive approaches to risk mitigation. Despite this potential, the study highlights several challenges, including issues related to data quality, system integration, and the need for specialized skills. The study concludes by offering practical recommendations for industry stakeholders, such as investing in data infrastructure and promoting innovation, and suggests future research on the broader adoption of predictive analytics across other areas of the oil and gas value chain.

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