Natural gas wells have a propensity to “freeze” or “shut-in” deferring gas production and incurring intervention costs to disperse the obstruction and/or install a downhole pump. Predicting when each natural gas well is at risk for freezing and when it is likely to need a downhole pump installed is a challenging problem but knowing the risks in advance enables effective prevention. The goal was to predict maintenance requirements three months out to optimize maintenance resources to achieve the greatest benefit.
Over a terabyte of sensor data was harnessed to identify and predict well freeze events on about 1,700 wells. The data was mostly from wellhead and separator tanks and included temperatures, pressures, tank levels, and flow rates. Also, operational reports of freezes were provided, and records of well design, well environment, and production history played predictive roles in the models. The data was large, diverse, noisy, often erroneous, and incomplete. With input from the client, key ratios that affect the well’s propensity to freeze within the next three months were calculated and we were able to predict well freezing several times better than any previous efforts given the available data. During the model building process, the insights about predictive relationships were reviewed periodically with client subject matter experts and models were thoroughly vetted by the client using a blind validation process.
Actionable insights about well production and freeze propensity were discovered. Predictions for optimizing the timing of plunger pump installation was projected to reduce production deferrals by 15-20%. The Downhole Freeze model had a 70% success rate. Its insight helped the client prioritize well interventions and optimize maintenance resources to reduce freeze remediation costs. It was projected to save 600,000 MSCF (million standard cubic feet) in deferred gas volume annually.