Sometimes offshore oil wells break down and need repair such as when a pump fails. If spare parts are not readily available, this can cause unwanted downtime that is expensive in terms of yield failure and repair costs.We show how costs can be minimized by predicting the status of pumps up to four weeks in advance– allowing preventive maintenance to be performed. This is made possible by using a mathematical model of the pumping operation using automated machine learning methods. This method was applied to shallow-water offshore oil wells in the Dagang oilfield covering 34,629 km2in China. We consider data for 5 oil-wells of a shallow water oil-rig in Dagang operated by PetroChina.
For complete details on this case study, go to: Increase in Oil Production Yield