Machine learning for property modeling combines tried and tested geostatistical methods with machine learning to change the paradigm of reservoir property modeling
The novel ‘embedding’ of geostatistical models into a decision forest-based tool removes the need for extensive data preparation tasks and enables the use of an unlimited number of variables. The solution reduces the workflow turnaround time from days to minutes and uses all available data to provide better constrained and predictive property models, unbiased predictions of sweet spots and a realistic quantification of uncertainty.
Watch this webinar to see how Machine Learning for Property Modeling can change the paradigm of your modeling workflow.
Presenters: Aaron Alee, product manager, subsurface modeling, SLB and David Marquez, product champion, reservoir modeling, SLB
