基于状态空间重构和BH-LSSVM的动态液位确定算法研究

Tong Wang, Haozhe Lai, Zijian Jiang
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引用次数: 1

摘要

油井的动态液位对潜水电机至关重要。动态液位预测是一个热门的研究方向。本文提出了一种将状态空间重构与黑洞最小二乘支持向量机(BH-LSSVM)算法相结合的动态液位短期确定方法。混沌时间序列需要在状态空间中重构。然后基于重构的状态空间数据,采用BH-LSSVM算法动态确定液位。仿真结果表明,该算法对油井动态液位的测量具有较高的精度。满足了油井作业的要求。它可用于油井中动态液位的测量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An algorithm study for determination of dynamic fluid level based on the state space reconstruction and BH-LSSVM
The dynamic fluid level of oil well is essential for the submersible motor. The prediction of dynamic fluid level is a popular research direction. This paper describes an approach to short-termly determine the dynamic fluid level by using the algorithm which combines the state space reconstruction and black hole least squares support vector machine (BH-LSSVM) algorithm together. The chaotic time series has to be reconstructed in the state space. Then based on the data of the reconstructed state space, the fluid levels will be determined dynamically, by using BH-LSSVM algorithm. The simulation results show that this algorithm has much higher accuracy on measurement of dynamic fluid level for oil well. It fulfills the requirements of the oil-well task. It can be deployed in oil well to measure the dynamic fluid level.
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