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 utilized its big data ecosystem, enhanced by machine learning capabilities, to analyze extensive historical data encompassing numerous variables under continuous conditions. This analysis aimed to identify patterns and assess the likelihood of oil-in-water incidents, enabling the formulation of targeted mitigation strategies tailored to specific conditions.

By developing predictive analytics, the initiative sought to anticipate such incidents in advance, affording operators sufficient time to implement preventive measures. The pilot project anticipated an anticipated increase in output ranging from 0.25% to 0.5%, along with additional time for pursuing other activities to enhance production efficiency.