【摘要】 准确的水质预测是评估油田联合站采出水处理效果的重要依据,为水质预警提供科学依据。传统方法存在主观性强和耗时性长等缺点,现有部分研究借助于机器学习方法,但对数据噪声和数据非线性考虑不足。本研究提出一种基于两层分解算法与改进支持向量机相结合的预测方法。通过两层分解算法消除冗余噪声,提取初始数据主要特征。利用分层抽样对原始数据集进行划分,避免传统随机抽样引起的样本偏差。采用改进粒子群算法优选支持向量机参数,提高全局收敛能力。针对桩西采油厂联合站4个案例,依据相对误差、平均绝对百分比误差和决定系数3个评价指标对提出的预测方法展开准确性评价,基于4个案例3个指标值的平均值分别为-0.38%、5.23%和0.82%。相比于现有主流机器学习方法,提出的预测方法具有较高的预测精度。更多还原

【Abstract】 The accurate prediction of the produced water quality is an important basis for evaluation of the treatment effect of the produced water at the oilfield joint station, which can provide a scientific basis for early warning of water quality. In the traditional method, we can see that the prediction of the oilfield produced water quality is mainly based on the experience of experts,however, there is no doubt that this method has a strong personal subjectivity so it is difficult to reach an accurate prediction of the quality of the produced water. There is also a part of existing studies to measure whether the produced water quality is up to the relevant standard. However, this method has the disadvantage of taking a long time so that it is not conducive for the efficient development of on-site work. Now there is a part of the existing research with the help of machine learning algorithms, but the situation of data noise and data non-linearity is not fully considered in these methods. In response to the above problems, a novel method for water quality prediction is put forward in this paper, which is based on the combination of the two-layer decomposition method and the modified support vector machine(SVM) algorithm. Through the two-layer decomposition method put forward above, the redundant noise in the prediction process can be eliminated effectively, and at the same time the major features of the original data can be extracted. The method of stratified sampling is used to divide the original dataset so as to avoid the sample deviation caused by the method of traditional random sampling. A modified particle swarm algorithm is applied to optimize the parameters of the SVM so that the global convergence ability can be improved by this algorithm. On the basis of the four cases of the Zhuangxi oil production plant joint station, the prediction accuracy of this method is evaluated in the light of three evaluation indexes: the relative error, the average absolute percentage error and the determination coefficient. On the basis of the average values of these 4 cases on the three indicators are-0.38%, 5.23% and 0.82%, respectively. Compared with the existing mainstream machine learning algorithms, we can see that the method in this paper has higher prediction accuracy.更多还原