Conventional fault diagnosis and production calculation of an oil well can be conducted with the surface dynamometer cards, which are obtained by load sensor installed on the horse head. This method to measure the dynamometer cards is limited by the sensor maintenance and calibration, battery replacement, and safety hazards for staff. As the basic parameter of the oil extraction industry, electric parameters have the advantages of low cost and high efficiency. So the inversions of dynamometer card with electric parameters are attracting more and more attention. In order to solve the problem of insufficient data and consider the realtime performance in the actual oil extraction process, this paper proposes a novel hybrid model which consists of two parts: the mechanism model of polished rod load and the suspension displacement calculated with the space vector equations of motor and a data-dependent kernel online sequential extreme learning machine (DDKOS-ELM) model proposed to correct the output error of the mechanism model, which improves the kernel function selection and makes it real-time. Thus, the highlights of this paper can be summed up in two points: (1) under the circumstance of the bottom dead point detection without sensors, the mechanism modeling of the polished rod load and suspension displacement has been carried out from the perspective of mathematical model of AC motor; (2) a novel data-driven model based on data-dependent kernel online sequential extreme learning machine (DDKOS-ELM) has been proposed to improve the kernel functions selection. The coefficients in the data-dependent kernel function are optimized with improved free search algorithm (IFSA). The proposed hybrid model has been applied to a normal working oil well and the prediction results show better accuracy than the pure data-driven model and mechanism model.
Conventional fault diagnosis methods of sucker rod pump (SRP) mainly focus the operating status of oil well by identifying the dynamometer cards (DCs), which are limited by the sensor maintenance and calibration, battery replacement and safety hazards for staff. Motor power, as the most basic parameter providing the energy source for the oil well, is directly related to the real-time operation state of oil well. Therefore, a novel deep and broad learning system (DBLS) based on motor power data for fault diagnosis of sucker rod pump is proposed in this paper. Considering the key parameters such as mechanical wear and balance weight, the motor power data are labeled by the DCs with typical working conditions. Furthermore, CNN-based feature extractor is designed to make up for the lack of expert experience in motor power, which is obtained by merging the output of the CNNs with the manual features extracted based on mechanical analysis. And then the broad learning system is employed as the classifier to solve the problem of realtime update of system structure. Finally, a dataset containing six different working states collected from the oilfield by a self-developed device is employed to verify the proposed method experimentally and compared with other methods.
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