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Reinforcement learning based optimization algorithm for maintenance tasks scheduling in coalbed methane gas field
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AI
DOI：10.1016/j.compchemeng.2022.108131.png)
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Abstract
En 中文
The Coalbed Methane (CBM) well maintenance tasks scheduling optimization is of importance for improving the CBM production efficiency. Traditionally, this problem is addressed using mathematical model based classical optimization method or some meta-heuristic algorithms. However, due to the large-scale nature of this problem, these trials often fail in practical use. Therefore, the Q-learning algorithm based solving method is proposed in this paper. An interactive environment for reinforcement learning is constructed. To validate the effectiveness of proposed method, scenarios with different scales are provided. For the cases with 10, 14, 20, 30, 47, 60, 80 and 100 maintenance tasks respectively, the required solution time is 3.66 s, 4.94 s, 7.66 s, 12.48 s, 21.82 s, 30.82 s, 48.33 s, 73.17 s respectively. The proposed Q-learning algorithm is insensitive with the problem scale, which is promising. Moreover, the Q-learning based algorithm is more efficient than the traditional algorithm along with the increase of the number of tasks.
Keywords:
Coalbed methane gas field
Maintenance tasks scheduling
Mathematical model
Reinforcement learning
Q -learning
Journal
C
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