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Application of dynamic liquid level prediction model based on improved SVR in sucker rod pump oil wells

机译:基于改进SVR的动态液位预测模型在抽油杆泵油井中的应用。

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This study built a dynamic liquid level prediction model based on improved SVR (Support Victor Regression) in sucker rod pump wells. This modeling method adopted sliding window to limit the number of samples and applied genetic algorithm to realize automatic optimization of C ande which are parameters of SVR. Through the simulation experiment, we verified the effectiveness of the modeling method and improved the precision of the model. After a period of actual operating in some oilfield, good results were obtained and the precision could perfectly meet the requirement of oil production.
机译:本研究在抽油杆泵井中建立了基于改进的SVR(Support Victor回归)的动态液位预测模型。该建模方法采用滑动窗来限制样本数量,并应用遗传算法来实现作为SVR参数的C和e的自动优化。通过仿真实验,验证了建模方法的有效性,提高了模型的精度。经过在某油田实际运行一段时间后,取得了良好的效果,精度可以完全满足石油生产的要求。

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