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Few-shot working condition recognition of a sucker-rod pumping system based on a 4-dimensional time-frequency signature and metalearning convolutional shrinkage neural network.
- Source :
-
Petroleum Science (KeAi Communications Co.) . Apr2023, Vol. 20 Issue 2, p1142-1154. 13p. - Publication Year :
- 2023
-
Abstract
- The accurate and intelligent identification of the working conditions of a sucker-rod pumping system is necessary. As onshore oil extraction gradually enters its mid-to late-stage, the cost required to train a deep learning working condition recognition model for pumping wells by obtaining enough new working condition samples is expensive. For the few-shot problem and large calculation issues of new working conditions of oil wells, a working condition recognition method for pumping unit wells based on a 4-dimensional time-frequency signature (4D-TFS) and meta-learning convolutional shrinkage neural network (ML-CSNN) is proposed. First, the measured pumping unit well workup data are converted into 4D-TFS data, and the initial feature extraction task is performed while compressing the data. Subsequently, a convolutional shrinkage neural network (CSNN) with a specific structure that can ablate low-frequency features is designed to extract working conditions features. Finally, a meta-learning finetuning framework for learning the network parameters that are susceptible to task changes is merged into the CSNN to solve the few-shot issue. The results of the experiments demonstrate that the trained ML-CSNN has good recognition accuracy and generalization ability for few-shot working condition recognition. More specifically, in the case of lower computational complexity, only few-shot samples are needed to fine-tune the network parameters, and the model can be quickly adapted to new classes of well conditions. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 16725107
- Volume :
- 20
- Issue :
- 2
- Database :
- Academic Search Index
- Journal :
- Petroleum Science (KeAi Communications Co.)
- Publication Type :
- Academic Journal
- Accession number :
- 164212486
- Full Text :
- https://doi.org/10.1016/j.petsci.2023.02.017