Imbalanced Working States Recognition of Sucker Rod Well Dynamometer Cards Based on Data Generation and Diversity Augmentation

Abstract

Summary During sucker rod pump production, there is a commonly seen problem of class imbalance, which refers to the differences in the amount of data accumulated under different working conditions. This problem in rod pump diagnosis can lead to unsatisfactory classification results of surface dynamometer cards under working conditions with fewer samples. Therefore, this study adopts the conditional generative adversarial nets (CGANs) improved by mini-batch method to address the problem of class imbalance. CGAN is an efficient method of multiclass data generation, which learns the properties of dynamometer cards by training the generator and discriminator networks. CGAN is modified by mini-batch strategy to avoid mode collapse and enable the interaction among input samples of discriminator, so that the generated dynamometer cards can be much more diverse. Results show that the shapes of generated dynamometer cards are basically consistent with those of real samples, and the structural similarity (SSIM) among the generated samples decreases, indicating that the generated dynamometer cards have more types of shape. Meanwhile, as the generated dynamometer cards become more diverse, their differences with real samples in data distribution are reduced, according to the calculation of sliced Wasserstein (SW) distance. Based on real and generated dynamometer cards, we developed the classifiers for working condition diagnosis of rod pump through convolutional neural network (CNN). The classification results of the validation set indicate that without the mini-batch method, the recall of generated categories for pump hitting down and leakage has increased by 12 and 5.3%, respectively; in contrast, with the mini-batch method, the recall has increased more obviously by 7, 24, and 2%, respectively, for gas lock, pump hitting down, and leakage. Our research results have demonstrated that the proposed method can effectively solve the problem of insufficient data accumulation in the oil field.