| Summary: | Nowadays, rod pump is widely used in oilfield. Since most oil production equipment like pumping pumps are distributed in the wild, they are usually checked by manual inspection. In the event of a faults, relying solely on labor to observe the indicator diagrams and determine the fault will waste a lot of human and financial resources. If it is not discovered in time, it will cause serious damage to oil exploitation, even shutdown. Indicator diagrams can reflect the working state of the rod pumping well, which can effectively reflect various faults of the pumping well. This paper diagnoses the faults of pumping wells by classifying and identifying the indicator diagrams. Because support vector machine (SVM) has good effect on classification and recognition of small sample data and nonlinear data, this paper uses SVM for classification, and uses the chicken swarm optimization (CSO) to optimize support for the problem that the SVM parameters are difficult to determine. Aiming at the problems of traditional CSO in solving high-dimensional optimization problems, such as premature and rough precision, an improved CSO is proposed. The traditional CSO, particle swarm optimization (PSO) and bat algorithm (BA) are used to compare it. The simulation proves that the improved CSO has good optimization effect and is superior to the other three optimization algorithms.
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