2023/09/01 by Bruno Machado Pacheco, Pacheco, Bruno Machado, Laio Oriel Seman +3
Engineering · #Enhanced Oil Recovery Techniques #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Oil and Gas Production Techniques #Optimization and Control (math.OC) #Reservoir Engineering and Simulation Methods
paper · pdf · doi:10.48550/arxiv.2309.00197
openalex publication_date 2023/09/01 · openalex created_date 2023/09/07 · openalex updated_date 2026/07/28
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.