JMP in the Upstream 02: Machine Learning Approach to Determine Decline Curves
It is understood that heterogeneity exists at all scales in the natural environment. Whether at the pore scale or basin scale, rarely can a geologist ...
Peter_PolitoIt is understood that heterogeneity exists at all scales in the natural environment. Whether at the pore scale or basin scale, rarely can a geologist ...
Peter_PolitoThe concept of pore pressure is a bit like the phases of the moon—most folks know there are phases, they're about a month long, there is a new moon, f...
Peter_PolitoXGBoost is a (relatively) new machine learning protocol that is only beginning to be deployed in the Upstream Energy and Production industry. This dec...
Peter_PolitoOptimizing machine learning hyperparameters is an important and sometimes fraught process. Variation in a single hyperparameter can greatly alter the ...
Peter_PolitoIn this episode of JMP in the Upstream, Bill Worley (Sr. Systems Engineer) showcases the add-in he co-developed for analyzing spectral data in JMP. He...
Peter_Politon this episode of JMP in the Upstream, Peter Polito (Sr. Systems Engineer/Geologist) walks us through a methodology for predicting downhole hydrocarbo...
Peter_Polito