Gas-oil separation plant virtual water cut predictor based on supervised learning framework on time series data

A computer system comprising one or more processors, wherein the one or more processors are configured to perform operations of:

• accessing data from a gas oil separation plant (GOSP) facility, wherein the data includes measurements at various locations inside the GOSP facility and measurements of water cut of the GOSP facility;
• selecting, based on feature engineering, a subset of features corresponding to the measurements at various locations inside the GOSP facility, wherein the subset of features are more likely to impact the water cut of the GOSP facility than unselected features;
• and
• based on the subset of features, training a predictive model capable of predicting the water cut of the GOSP facility based on the measurements of water cut of the GOSP facility, wherein the training is based on, at least in part, (i) a subset of the measurements at various locations inside the GOSP facility and (ii) a subset of the measurements of water cut of the GOSP facility.