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

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.

A Working Condition Diagnosis Model of Sucker Rod Pumping Wells Based on Deep Learning

Summary Monitoring the working conditions of sucker rod pumping wells in a timely and accurate manner is important for oil production. With the development of smart oil fields, more and more sensors are installed on the well, and the monitored data are continuously transmitted to the data center to form big data. In this work, we aim to utilize the big data collected during oil well production and a deep learning technique to build a new generation of intelligent diagnosis model to monitor working condition of sucker rod pumping wells. More than 5×106 of well monitoring records, which covers information from about 1 year for more than 300 wells in an oilfield block, are collected and preprocessed. To show the dynamic changes of the working conditions for the wells, the overlay dynamometer card is proposed and plotted for each data record. The working conditions are divided into 30 types, and the corresponding data set is created. An intelligent diagnosis model using the convolutional neural network (CNN), one of the deep learning frameworks, is proposed. By the convolution and pooling operation, the CNN can extract features of an image implicitly without human effort and prior knowledge. That makes a CNN very suitable for the recognition of the overlay dynamometer cards. The architecture for a working condition diagnosis CNN model is designed. The CNN model consists of 14 layers with six convolutional layers, three pooling layers, and three fully connected layers. The total number of neurons is more than 1.7×106. The overlay dynamometer card data set is used to train and validate the CNN model. The accuracy and efficiency of the model are evaluated. Both the training and validation accuracies of the CNN model are greater than 99% after 10 training epochs. The average training elapsed time for an epoch is 8909.5 seconds, and the average time to diagnosis a sample is 1.3 milliseconds. Based on the trained CNN model, a working condition monitoring software for a sucker rod pumping well is developed. The software runs 7 × 24 hours to diagnosis the working conditions of wells and post a warning to users. It also has a feedback learning workflow to update the CNN model regularly to improve its performance. The on-site run shows that the actual accuracy of the CNN model is greater than 90%.

Calculation Method for Inflow Performance Relationship in Sucker Rod Pump Wells Based on Real-Time Monitoring Dynamometer Card

Based on the informatization and intelligent construction of an oilfield, this paper proposes a new method for calculating inflow performance relationship in sucker rod pump wells, which solves the limitations of current IPR curve calculation method in practical application. By analyzing the forming principle of the dynamometer card, the plate of abnormal dynamometer card is created innovatively, and the recognition model of abnormal dynamometer card based on “feature recognition” is established to ensure the accuracy of the dynamometer card. By analyzing the curvature of each point on the curve of downhole pump dynamometer card, the opening and closing points of standing valve and traveling valve are determined, and the models for calculating fluid production and bottom hole flowing pressure are established to obtain the data of fluid production and bottom hole flowing pressure of sucker rod pump wells. Finally, a calculation model of inflow performance relationship fitted with the calculated fluid production and bottom hole flowing pressure data based on genetic algorithm is established to realize calculation of oil well inflow performance relationship curve. The field application and analysis results show that the inflow performance relationship curve calculated by the model in this paper fits well with the measured data points, indicating that the calculation model has high accuracy and can provide theoretical and technical support for the field. Moreover, the real-time acquisition of dynamometer cards can provide real-time data source for this method, improve the timeliness of oil well production analysis, and help to reduce the production management costs and improve the production efficiency and benefit.

Automatic Recognition of Sucker-Rod Pumping System Working Conditions Using Dynamometer Cards with Transfer Learning and SVM

Sucker-rod pumping systems are the most widely applied artificial lift equipment in the oil and gas industry. Accurate and intelligent working condition recognition of pumping systems imposes major impacts on oilfield production benefits and efficiency. The shape of dynamometer card reflects the working conditions of sucker-rod pumping systems, and different conditions can be indicated by their typical card characteristics. In traditional identification methods, however, features are manually extracted based on specialist experience and domain knowledge. In this paper, an automatic fault diagnosis method is proposed to recognize the working conditions of sucker-rod pumping systems with massive dynamometer card data collected by sensors. Firstly, AlexNet-based transfer learning is adopted to automatically extract representative features from various dynamometer cards. Secondly, with the extracted features, error-correcting output codes model-based SVM is designed to identify the working conditions and improve the fault diagnosis accuracy and efficiency. The proposed AlexNet-SVM algorithm is validated against a real dataset from an oilfield. The results reveal that the proposed method reduces the need for human labor and improves the recognition accuracy.

A Novel Method for Diagnosis of Sucker-Rod Pumping Systems Based on the Polished-Rod Load Vibration in Vertical Wells

Summary The pump dynamometer card is a direct reflection of the operating conditions of the downhole pump, which is important for the diagnosis of sucker-rod pumping systems. In this paper, we propose a novel diagnostic method based on the estimation of the parameters from the polished-rod load vibration signal of sucker-rod pumping systems in a vertical well. In this study, we deduce a new analytic solution of the 1D wave equation of the sucker-rod string, which can be used for the predictive and diagnostic analyses. The relationship between the polished-rod load vibration and the pump equivalent impulse load based on the analytic solution is studied, and the diagnostic parameter estimating method is proposed. Therefore, the pump dynamometer card calculated method based on the surface dynamometer card is realized. This study shows that the method is efficient.