New South Wales/Australian Capital Section

Persistent Bias in Probabilistic Production Forecasting and Simple Methods to Overcome It

The Distinguished Lecturer program brings world-class expertise to the global energy community, inspiring innovation and knowledge sharing. Through engaging presentations and thought leadership, it empowers engineers, scientists, and energy professionals to advance technical excellence in oil, gas, and related resources—driving progress toward a safe, secure, and sustainable energy future. Featured Speaker Reidar Bratvold is a Professor of Investment and Decision Analysis at the University of Stavanger, specialising in decision analysis, data analytics, and the behavioural challenges of decision-making. After 15 years in technical and leadership roles within the industry, Reidar transitioned to academia, where he has made significant contributions to his field. He is the co-author of Making Good Decisions , has served as an SPE Distinguished Lecturer, and was honoured with the 2017 SPE Management & Information Award. Reidar is a Fellow of both the Society of Decision Professionals and the Norwegian Academy of Technological Sciences. He earned his Ph.D. in Engineering and an M.Sc. in Mathematics from Stanford University. Abstract I would like the members to take away one key idea: Unbiased probabilistic production forecasts are a crucial component in making value-creating investment decisions , but typical methods result in persistent, value-destroying biases. There are quick and easy ways to make accurate, unbiased forecasts. Increased awareness of uncertainty, combined with increasingly sophisticated tools and models for quantifying it, is causing a shift from deterministic to probabilistic production forecasting. For these forecasts to lead to better investment decisions (e.g. assessing how much it’s worth paying to reduce uncertainty, or incorporating flexibility to manage it), they need to be an accurate (unbiased) representation of the uncertainty. However, our industry has a general history of overconfidence (ranges too narrow) and, more damaging for value creation, optimism (consistent over-estimation). A large dataset of historical probabilistic production forecasts was investigated for potential bias by comparing them with actual outcomes. They were found to be both optimistic and overconfident, bringing into question their usefulness for decision-making and the value of the sophisticated uncertainty modelling techniques that were used to generate them. Fortunately, there are quick and easy methods for creating accurate probabilistic forecasts. We describe these and show that they would have produced accurate forecasts for our case study fields (they do not make use of knowing the actual outcomes!). Finally, preliminary results from a similar analysis of renewables projects indicate that the same problems exist. This could significantly impact government and company investment decisions and policy development in renewable projects. Event Details 📅 Date: Thursday, 5 February 2026 🕒 Time: 12:00 PM – 2:00 PM 📍 Location: Club York, Level 2, 95–99 York St, Sydney NSW 2000 Registration Deadlines: Early Bird: Closes Friday 23 January 2026 Final Registration: Closes Friday 30 January 2026