Embodiments relate to acquiring well, reservoir and production data, synthesizing training and
test data, and then constructing, training, and utilizing
machine-
learning models to: (i) predict whether or not
gas lift should be applied to facilitate the production of subsurface fluids from an
oil well, (ii) predict an optimal range of
gas lift values to be used in the production of fluids from the
oil well, and (iii) predict an optimal liquid
production rate for the
oil well when the
gas lift value is within the predicted optimal range. Unlike traditional approaches, the disclosed embodiments do not require the use of well interventions, which eliminates production losses, delays, and costs. The disclosed embodiments also avoid the delays, biases, and non-optimized values associated with existing trial-and-error-based approaches to gas lift injection optimization. Disclosed embodiments enable the efficient and reliable determination of the optimal range of gas lift values and the optimal liquid
production rate.