Journal Article

Measured multi-source semi-supervised working condition recognition based on curvelet pooling and attention mechanism learning

Zhou, Bin, Wang, Yanjiang, Yang, Shuo, Liu, Weifeng

2025

Overview

To identify various oil well working conditions more accurately and practically from massive image data collected by multiple measured information sources of sucker-rod pumping wells, this paper proposes a working condition recognition method with three key aspects: curvelet pooling optimization technology, multi-source attention mechanism fusion feature extraction technology, and multi-source semi-supervised classification deep learning. Specifically: (a) Curvelet pooling optimization technology. We introduce the second-generation curvelet transform into the ResNet-50 pooling layer and adopt a collaborative learning pooling strategy of low-frequency and high-frequency information from the raw data decomposed via curvelet transform instead of max-pooling. This enhances the neural network’s capability to capture detailed features of complex image data. (b) Multi-source attention mechanism fusion feature extraction technology. We selected two information sources: measured ground dynamometer cards and measured electrical power cards. The multi-head self-attention mechanism enables interactive complementarity between curvelet-decomposed image data from each information source, while achieving dynamic weighted fusion of the interactive complementary data via the adaptive attention mechanism. This process yields optimal global feature representations of multi-source fused data. (c) Multi-source semi-supervised classification deep learning. By integrating multi-source fused feature data with a semi-supervised classification algorithm based on the dual strategy of dynamic adjustment of pseudo-label confidence and self-adaptive class fairness regularization, the method leverages abundant multi-source unlabeled samples to improve model classification performance and generalization ability under limited labeled training samples. This further enhances the accuracy and practicality of condition recognition. Experimental data were collected from a high-pressure, low-permeability, thin oil reservoir block in an oilfield in China. Extensive experiments demonstrate that the proposed method efficiently processes measured information source data in the sucker-rod pumping production system, improves the performance of traditional deep learning frameworks, explores the intrinsic correlations among multiple measured information source data of oil wells, and utilizes massive unlabeled working condition data to enhance the working condition recognition effect and engineering practicability with a minimal number of labeled samples. Code is available at https://github.com/Yoick/AMMFFECP .

• Publisher: Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
• Subject: 639/166 / 639/4077 / 639/705 / Accuracy / Attention mechanism / Classification / Collaborative learning / Complementarity / Curvelet pooling / Decomposition / Deep learning / Electric power / Engineering / Humanities and Social Sciences / Information processing / Information sources / Interactive learning / Machine learning / Multi-source feature fusion / multidisciplinary / Neural networks / Oil and gas fields / Oil reservoirs / Oil wells / Oils & fats / Science / Science (multidisciplinary) / Semi-supervised learning / Signal processing / Sucker-rod pumping well working condition recognition / Wavelet transforms / Working conditions