ConocoPhillips Predictive Equipment Failures Challenge

Predict downhole equipment failures using sensor data

Background (Copied from Kaggle Description)

80% of producing oil wells in the United States are classified as stripper wells. Stripper wells produce low volumes at the well level, but at an aggregate level these wells are responsible for a significant percentage of domestic oil production.

Stripper wells are attractive to a company due to their low operational costs and low capital intensity - ultimately providing a source of steady cash flow to fund operations that require more funds to get off the ground.

At ConocoPhillips, our West Texas Conventional operations serve as a source of organic cash flow to fund more expensive projects in the Delaware Basin and other unconventional plays across the United States. As a company, it is vital that this steady, low cost form of cash has a constant presence.

As with all mechanical equipment, things break and when things break money is lost in the form of repairs and lost oil production. When costs go up cash goes down, but how can we predict when equipment will fail and use this information to drive down our costs?