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Maximizing oil production from gas-lifted oil wells entails solvingMixed-Integer Linear Programs (MILPs). As the parameters of the wells, such asthe basic-sediment-to-water ratio and the gas-oil ratio, are updated, theproblems must be repeatedly solved. Instead of relying on costly exact methodsor the accuracy of general approximate methods, in this paper, we propose atailor-made heuristic solution based on deep learning models trained to providevalues to all integer variables given varying well parameters, early-fixing theinteger variables and, thus, reducing the original problem to a linear program(LP). We propose two approaches for developing the learning-based heuristic: asupervised learning approach, which requires the optimal integer values forseveral instances of the original problem in the training set, and aweakly-supervised learning approach, which requires only solutions for theearly-fixed linear problems with random assignments for the integer variables.Our results show a runtime reduction of 71.11% Furthermore, theweakly-supervised learning model provided significant values for early fixing,despite never seeing the optimal values during training.

Deep-learning-based Early Fixing for Gas-lifted Oil Production Optimization: Supervised and Weakly-supervised Approaches