标题： 基于深度学习的气举采油优化早期修复：监督与弱监督方法 显示英文标题

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

摘要： 从气举油井中最大化原油产量涉及求解混合整数线性规划（MILP）。随着油井的参数（如基础沉积物与水的比率和气油比）更新，这些问题必须被反复求解。而不是依赖于昂贵的精确方法或一般近似方法的准确性，在本文中，我们提出了一种量身定制的启发式解决方案，该方案基于深度学习模型，这些模型被训练以在给定不同油井参数时提供所有整数变量的值，提前固定整数变量，从而将原始问题简化为线性规划（LP）。我们提出了两种开发基于学习的启发式的方法：一种是监督学习方法，这需要在训练集中几个原始问题实例的最优整数值；另一种是弱监督学习方法，这只需要对提前固定的线性问题的随机分配整数变量的解。我们的结果表明运行时间减少了71.11%。此外，尽管在训练期间从未见过最优值，但弱监督学习模型仍为提前固定提供了显著的价值。 显示英文摘要

摘要： 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.