Subsurface Uncertainty Quantification Using Machine Learning: Improved Reservoir Management Through Data Driven Predictions 2025

Description

Highlights

4-day course tailored for energy sector professionals to enhance expertise in managing subsurface uncertainties. Machine learning techniques to quantify subsurface uncertainty and improve reservoir management. Covers regression, classification, and time series analysis with a focus on Conformal Prediction. Addresses data acquisition limitations through data augmentation and advanced interpolation. Improves prediction accuracy by accounting for measurement errors in well logs and core data. Equips participants to effectively communicate uncertainty and make informed decisions. Delivered face-to-face or through Virtual Instructor Led Training (VILT). Includes 32 hours of training, equivalent to 32 CPD points. Participants need access to a computer with internet and a Google account. Taught by an expert instructor with extensive experience in geophysics and petroleum engineering.