This Darcy Forum was focused on probabilistic forecasting and uncertainty management in the oil and gas industry. The presentation highlighted the challenges and benefits of probabilistic forecasting and the delivery models for probabilistic software. The presenters also covered cross-functional applications of uncertainty-capable software in the industry, sharing insights from various operators and software providers, including Powersim and their pForecast solution. The importance of accurate forecasting in minimizing risks and maximizing profitability for E&P companies was emphasized throughout the presentation.

One of the key takeaways from the event was the need for a multidisciplinary approach to successfully manage uncertainties in the industry. To this end, the event was attended by subsurface, production, drilling, completions engineers, planning analysts, and asset managers.

During the event, survey responses were gathered to better understand how companies are currently managing uncertainties in production forecasting. The survey showed that among the topics considered important for companies, production had the highest response rate at 77%, followed by subsurface at 71%, drilling and completions at 68%, and projects at 52%.

Regarding the methods used for modeling uncertainty associated with production forecasting, the survey revealed that applying deviation to the deterministic analysis continues being one of the most popular method head to head with Monte Carlo simulations at 48%, followed by Linear Regression at 32%, and time series analysis at 28%.

The survey also asked about the use of other tools for analyzing uncertainty, with 55% of respondents reporting the use of Crystal Ball, followed by 20% for Rose & Associates and 15% for @Risk and also for other tools.

Overall, the event provided valuable insights into the challenges and benefits of probabilistic forecasting and the importance of accurate forecasting in the oil and gas industry. The survey responses also shed light on how companies are currently managing uncertainties in production forecasting and the methods and tools they are using to do so.