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

arXiv: https://arxiv.org/abs/2309.00197 · HTML: https://mlsanity.com/paper/2309.00197/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 11 references that are in the map.
- First posted on arXiv 1 Sep 2023.

## Builds on (most cited first, 4 of 4)

- [Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon](https://mlsanity.com/paper/1811.06128/) (2018)
- [Learning to Accelerate Approximate Methods for Solving Integer Programming via Early Fixing](https://mlsanity.com/paper/2207.02087/) (2022)
- [Generative deep learning for decision making in gas networks](https://mlsanity.com/paper/2102.02125/) (2021)
- [Graph Neural Networks for the Offline Nanosatellite Task Scheduling Problem](https://mlsanity.com/paper/2303.13773/) (2023)

## Abstract (arXiv)

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.