000			01484nam a22001577a 4500
999			_c522131 _d522131
008			230313b ||||| |||| 00| 0 eng d
100			_aNatarajan, Ananth _938664
245			_aReference class forecasting and machine learning for improved offshore oil and gas megaproject planning: Methods and application
260			_aProject Management Journal
300			_a53(5), Oct, 2022: p.456-484
520			_aThis 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
650			_aMegaproject performance, Reference class forecasting, Oil and gas projects, Machine learning, Schedule and cost overruns, Megaproject performance forecasting, Planning, heuristics, biases. _936820
773			_aProject Management Journal
906			_aENERGY RESOURCES
942			_cAR