Abstract: How making full use of the multiple measured information sources obtained from the sucker-rod pumping wells based on deep learning is crucial for precisely recognizing the operating conditions. However, the existing deep learning-based operating condition recognition technology has the disadvantages of low accuracy and weak practicality owing to the limitations of methods for handling single-source or multi-source data, high demand for sufficient labeled data, and inability to make use of massive unknown operating condition data resources. To solve these problems, here we design a semi-supervised operating condition recognition method based on multi-source consistency deep learning. Specifically, on the basis of the framework of WideResNet28-2 convolutional neural network (CNN), the multi-head self-attention mechanism and feedforward neural network are first used to extract the deeper features of the measured dynamometer cards and the measured electrical power cards, respectively. Then, the consistency constraint loss based on cosine similarity measurement is introduced to ensure the maximum similarity of the final features expressed by different information sources. Next, the optimal global feature representation of multi-source fusion is obtained by learning the weights of the feature representations of different information sources through the adaptive attention mechanism. Finally, the fused multi-source feature combined with the multi-source semi-supervised class-aware contrastive learning is exploited to yield the operating condition recognition model. We test the proposed model with a dataset produced from an oilfield in China with a high-pressure and low permeability thin oil reservoir block. Experiments show that the method proposed can better learn the critical features of multiple measured information sources of oil wells, and further improve the operating condition identification performance by making full use of unknown operating condition data with a small amount of labeled data.

Jianguo Yang, Bin Zhou, Muhammad Tahir, Min Zhang, Xiao Zheng and Xinqian Liu, “Multi-Source Consistency Deep Learning for Semi-Supervised Operating Condition Recognition in Sucker-Rod Pumping Wells” International Journal of Advanced Computer Science and Applications(IJACSA), 15(12), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0151251

@article{Yang2024,
title = {Multi-Source Consistency Deep Learning for Semi-Supervised Operating Condition Recognition in Sucker-Rod Pumping Wells},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151251},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151251},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {12},
author = {Jianguo Yang and Bin Zhou and Muhammad Tahir and Min Zhang and Xiao Zheng and Xinqian Liu}
}