Reference class forecasting and machine learning for improved offshore oil and gas megaproject planning: Methods and application
By: Natarajan, Ananth
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Material type: 
BookPublisher: Project Management Journal Description: 53(5), Oct, 2022: p.456-484.Subject(s): Megaproject performance, Reference class forecasting, Oil and gas projects, Machine learning, Schedule and cost overruns, Megaproject performance forecasting, Planning, heuristics, biases
In:
Project Management JournalSummary: This article develops and describes rigorous oil and gas project forecasting methods. First, it builds a theoretical foundation by mapping megaproject performance literature to these projects. Second, it draws on heuristics and biases literature, using a questionnaire to demonstrate forecasting-related biases and principal-agent issues among industry project professionals. Third, it uses methodically collected project performance data to demonstrate that overrun distributions are non-normal and fat-tailed. Fourth, reference-class forecasting is demonstrated for cost and schedule uplifts. Finally, a predictive approach using machine learning (ML) considers project-specific factors to forecast the most likely cost and schedule overruns in a project.- Reproduced
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Indian Institute of Public Administration | 53(5), Oct, 2022: p.456-484 | Available | AR128379 |
This article develops and describes rigorous oil and gas project forecasting methods. First, it builds a theoretical foundation by mapping megaproject performance literature to these projects. Second, it draws on heuristics and biases literature, using a questionnaire to demonstrate forecasting-related biases and principal-agent issues among industry project professionals. Third, it uses methodically collected project performance data to demonstrate that overrun distributions are non-normal and fat-tailed. Fourth, reference-class forecasting is demonstrated for cost and schedule uplifts. Finally, a predictive approach using machine learning (ML) considers project-specific factors to forecast the most likely cost and schedule overruns in a project.- Reproduced
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