Full Reproduction of Surface Dynamometer Card Based on Periodic Electric Current Data

Abstract

Summary The surface dynamometer card is composed of ground load and ground displacement, which is of great significance to reflect the operation of rod pumping and the exploitation of crude oil. However, the current method of obtaining the surface dynamometer by sensors is a huge financial investment on the sensor installations and maintenance. In this paper, we propose an innovative method based on deep learning to reproduce the surface dynamometer card directly from electrical parameters. In our method, the convolution neural network is used as the basic layer to automatically extract the spatial characteristics of input data. A long short-term memory (LSTM) network as the core component is used for the output layer to consider the time dependence of the dynamometer card. Finally, the experimental shows that the proposed method achieves the mean relative error (MRE) of 4.00% on the real oil well data in A-oilfield, and the dynamometer card calculated by our model is basically consistent with the field data. In addition, the method has been tested in new wells with a rod pumping system, and the results show that the accuracy of the model is close to 90%, which has already greatly outperformed the previous methods.

Fault Diagnosis Method and Application of ESP Well Based on SPC Rules and Real-Time Data Fusion

“…Based on the calculation steps in Figure 11, calculate the probability of occurrence of different oil wells, different diagnosis time windows, and different working conditions by using formula (6), as shown in Table 7. e greater the fault probability value, the greater the possibility of such a fault.…”

“…Initially, engineers and managers used manual identification of current cards to diagnose the faults of ESP wells, but the real time and accuracy were poor [4]. With the continuous development of neural networks, a large number of experts and scholars have begun to study the use of neural networks for current card recognition [5,6]. It avoids the different interpretation results of the same ammeter card due to differences in personal knowledge and experience during manual identification.…”