Deep-learning-based Early Fixing for Gas-lifted Oil Production
  Optimization: Supervised and Weakly-supervised Approaches
Bruno Machado Pacheco Laio Oriel Seman Eduardo Camponogara
Maximizing oil production from gas-lifted oil wells entails solving
Mixed-Integer Linear Programs (MILPs). As the parameters of the wells, such as
the basic-sediment-to-water ratio and the gas-oil ratio, are updated, the
problems must be repeatedly solved. Instead of relying on costly exact methods
or the accuracy of general approximate methods, in this paper, we propose a
tailor-made heuristic solution based on deep learning models trained to provide
values to all integer variables given varying well parameters, early-fixing the
integer variables and, thus, reducing the original problem to a linear program
(LP). We propose two approaches for developing the learning-based heuristic: a
supervised learning approach, which requires the optimal integer values for
several instances of the original problem in the training set, and a
weakly-supervised learning approach, which requires only solutions for the
early-fixed linear problems with random assignments for the integer variables.
Our results show a runtime reduction of 71.11% Furthermore, the
weakly-supervised learning model provided significant values for early fixing,
despite never seeing the optimal values during training.