Machine Learning Takes On Oil, Gas Production Forecasting Role

As production decline rates accelerate for low-permeability reservoirs such as those in U.S. shale plays, machine learning neural networks can be a simpler, faster and as accurate alternative to traditional decline curve analysis for production forecasting, analysts say.

A common model used to forecast decline curves—the Arps model—has its challenges, according to Alexandre Ramos-Peon, senior analyst of shale research for Rystad Energy. These include infinite parameters used in the equation that introduce variables—such as varying well designs and completion techniques—which could impact production modeling.

“You need to come up with good parameters if you want to have reasonable forecasts,” Ramos-Peon said during a recent webinar.

The topic was addressed as production from legacy wells continues to decline. Oil production from legacy wells in the Permian Basin, for example, was projected to drop by 236,697 barrels per day in December, according to the U.S. Energy Information Administration’s latest Drilling Productivity report. Legacy gas production was also projected to fall by about 352 billion cubic feet per day.