What are the Top Seven Mistakes in Forecasting and How to Correct Them

This presentation will delve into common forecasting pitfalls and, more crucially, how to address model blindness. By reviewing seven more recent Carbon Capture and Storage (CCS) projects as well as Oil & Gas (O&G) projects over the past forty years, we identify prevalent trends. The talk will highlight frequent mistakes in analytical, empirical, and simulation models.
 
Faults, fractures, and formation damage tend to occur more often than initially anticipated in models. These occurrences, known as grey swan effects, are recognized but often overlooked phenomena that warrant closer examination.
 
Reservoir simulation is considered the pinnacle of integrating geological and engineering data. These simulation flow models are essential for various projects, including oil and gas, hydrology, nuclear waste disposal, geothermal, civil engineering, produced water disposal, CCS, and lithium projects. However, without calibration and "history matching," these models cannot reliably predict reservoir behavior due to "grey swan" effects and neglecting operational effects.
 
To mitigate this issue, we must carefully select surveillance data and honestly acknowledge the phenomena we are not history matching. The presentation will also explore the roles of empirical, analytical, and AI/machine learning methods in forecasting, ways to enhance our forecasts. Often with these above tools and more frequent focussed data we can “see” the grey swan effects earlier.
 
Thank you to our event sponsor: SAGA Wisdom!
 
Registration closes on Monday, April 28, 2025 at 8:00AM MDT.