When the oil content exceeds that level, the production rate must be reduced and the entire production team must work to resolve the issue. This problem was constraining production and consuming the energy of the production team on almost half of all operating days.
Whiteklay applied its IZAC framework, powered by machine learning, to spot patterns among hundreds of variables in continual conditions to analyze historical data to determine the probability of oil-in-water incidents, and to formulate the most effective mitigating actions, based on specific conditions. An algorithm was developed to predict the occurrence of such incidents early, giving the operator the time needed to prevent them altogether. The pilot result: an expected production increase of 0.25-0.5 percent, and more time to pursue other production-enhancing activities.