Recently, the team of Zhang Kai has made new progress in the field of smart wellbore diagnosis and optimization., and the related research has been published in the Geoenergy Science and Engineering. The paper is titled " Diagnosis of pumping machine working conditions based on transfer learning and ViT model ".
Innovation: Currently, the diagnosis of rod pump system conditions mainly relies on convolutional neural networks to identify dynagraphs. However, due to the problem of similar dynagraphs but different working conditions, the diagnostic accuracy is not satisfactory. In this study, to address this challenge, we combine transfer learning and Vision Transformer to develop a rod pump condition diagnosis method based on transfer learning and ViT models. We pre-train the ViT model using the ImageNet-1k dataset and fine-tune the weights of the model using actual dynagraph data to improve the diagnostic ability. This method can better solve the practical problems of dynagraphs and meet the needs of oilfield sites.
Abstract: The dynagraph card is an important tool for diagnosing work conditions in rod pump systems. However, a key problem is that actual dynagraph cards may have different working conditions yet exhibit similar graphs. This reduces the classification performance of existing models and leads to poor diagnostic accuracy. To solve this problem, we propose a novel method based on transfer learning and ViT models for diagnosing the working conditions of rod pumping systems. Specifically, we first pre-trained the ViT model using the ImageNet-1k dataset, and then fine-tuned the weights of the model using the actual dynagraph card dataset. This transfer learning way not only significantly reduce the training time of the model, but also effectively improve the accuracy of the working condition diagnosis. To evaluate the performance of the proposed method, we compare it with ResNet, DenseNet, and RegNet models. Our experimental results demonstrate that our method achieves a work condition diagnosis accuracy of 0.9060, which is higher than other methods by 0.2–0.3. Moreover, our method performs well on the problem of different work conditions but graphically similar dynagraph cards. Therefore, our transfer learning and ViT model-based method can better solve the practical problems of dynagraph cards and meet the needs of oilfield sites.
The Geoenergy Science and Engineering(formerly known as Journal of Petroleum Science and Engineering) covers the fields of petroleum and natural gas exploration, production and flow in its broadest possible sense. Topics include: reservoir engineering; reservoir simulation; rock mechanics; petrophysics; pore-level phenomena; well logging, testing and evaluation; mathematical modeling; enhanced oil and gas recovery; petroleum geology; compaction/diagenesis; petroleum economics; drilling and drilling fluids; thermodynamics and phase behavior; fluid mechanics; multi-phase flow in porous media; production engineering; formation evaluation; exploration methods; CO2 Sequestration in geological formations/sub-surface, etc. The latest impact factor of the journal is 5.168, and the average impact factor IF in the past 3 years is 3.646. This journal currently appears in the JCR Q1 or the Chinese Academy of Sciences ranking 2.
Paper link:
https://doi.org/10.1016/j.geoen.2023.211729
Citation:
Liming Zhang, Jinlian Wu, Kai Zhang, et al. Diagnosis of Pumping Machine Working Conditions Based on Transfer Learning and ViT Model[J]. Geoenergy Science and Engineering, 2023, 226: 211729.
