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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 real-time 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: 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; 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.

Electric-Parameter-Based Inversion of Dynamometer Card Using Hybrid Modeling for Beam Pumping System