Beam pumping units have been widely used in oilfields worldwide due to its simple structure, strong field adaptability, and convenient maintenance. Different energy-saving technologies have also been broadly applied in various beam pumping units. Among these energy-saving methods, the variable speed drive is one of the most acceptable techniques in the oil and gas industry. In this paper, the energy-saving technology of variable speed drives is discussed in detail for beam pumping units pointing out existing difficulties and current research status in kinematics. Three application examples of a variable speed drive in Daqing oilfield, the largest oilfield in China, is shown.

The beam pumping units applied in oilfield for more than 150 years, because it had the advantages of simple structure, reliable and durable. At present, it is still one of the most important artificial lift methods in the world. Due to the inherent structure of the beam pumping units, the balanced torque curve of gearbox has a bigger fluctuation ratio and negative torque that results low motor efficiency and load impact. Because the motor efficiency is directly proportional to the fluctuation ratio of gearbox torque, many researchers designed many types of energy-saving devices to improve the system efficiency. Therefore, based on the balance theory of gearbox torque, we introduced and discussed some significant energy-saving technologies, such as phased pumping units, dual-horsehead pumping units, shock absorber device, load reducer device, lower barbell pumping units, multi-balance device, flywheel-energy-storage device, variable speed drive device. Meanwhile, we also pointed out the future research direction.

The difficulty of collecting fault data samples is one of the application problems of the deep learning method in fault diagnosis of mechanical production; the second is that when the depth of the learning network increases, the network accuracy is saturated or even decreased. Therefore, based on the deep learning algorithm and the DenseNet model, this paper establishes a fault diagnosis model for the beam pumping unit through the transfer learning method. The model uses the global pooling layer as the classifier. The model is used to classify and test various working conditions such as wax deposition, pump leakage, insufficient liquid supply, and pump leakage in oil wells. The results show that the model can obtain a classification model with high accuracy by learning a limited number of sample data; in the case of uneven data samples, the model can also basically complete the task classification task accurately. Through the evaluation of the test set, the model has an average accuracy of more than 95% in identifying various working conditions.

Two new flavonoid glycosides, hypolaetin 4′-methyl ether 8-O-β-d-glucuronide 2″-sulfate (1) and hypolaetin 4′-methyl ether 3′-O-β-d-glucoside (2), and a new phenylpropanoid glucose ester, 1,6-diferuloyl glucose (3), were isolated from the leaves of Sterculia foetida L. Their structures were elucidated by spectroscopic analysis and chemical evidence.

There are more than 20 types of dynamometer card measured of sucker rod pumping (SRP) wells in oil fields, and some working conditions are very complicated. The common diagnosis model of SRP well based on dynamometer card recognition has low accuracy and recall rate of complicated working conditions. In order to improve the accuracy and recall rate of multi-condition diagnosis of SRP well and solve the problem of inseparable data attributes caused by traditional dynamometer card normalization methods, a new dynamometer card preprocessing method is proposed, which uses a clustering analysis algorithm to obtain multiple normalized dynamometer cards of the original dynamometer card and at the same time, adds a set of time-series dynamometer cards to enhance the separability of data. The dynamometer card preprocessing method combined with four deep convolutional neural networks are used to build a diagnosis model. Experiments are conducted under 24 different working conditions, the accuracy of our method is up to 95.8%, and the average recall rate of complicated working conditions is up to 93.1%, which is 13.6 and 35.3% higher than that of the model (AlexNet) built by the traditional preprocessing method. In addition, the preprocessing method of dynamometer card proposed is applicable to all deep learning models and machine learning models. Field applications show that our method is very effective for recalling abnormal working conditions, which is of great significance to the real demand for intelligent diagnosis of SRP well.