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摘要? 準(zhǔn)確的水質(zhì)預(yù)測(cè)是評(píng)估油田聯(lián)合站采出水處理效果的重要依據(jù),，為水質(zhì)預(yù)警提供科學(xué)依據(jù),。傳統(tǒng)方法存在主觀性強(qiáng)和耗時(shí)性長(zhǎng)等缺點(diǎn),，現(xiàn)有部分研究借助于機(jī)器學(xué)習(xí)方法，但對(duì)數(shù)據(jù)噪聲和數(shù)據(jù)非線性考慮不足,。本研究提出一種基于兩層分解算法與改進(jìn)支持向量機(jī)相結(jié)合的預(yù)測(cè)方法,。通過兩層分解算法消除冗余噪聲,，提取初始數(shù)據(jù)主要特征。利用分層抽樣對(duì)原始數(shù)據(jù)集進(jìn)行劃分,，避免傳統(tǒng)隨機(jī)抽樣引起的樣本偏差,。采用改進(jìn)粒子群算法優(yōu)選支持向量機(jī)參數(shù)，提高全局收斂能力,。針對(duì)樁西采油廠聯(lián)合站 4 個(gè)案例,，依據(jù)相對(duì)誤差、平均絕對(duì) 百分比誤差和決定系數(shù) 3 個(gè)評(píng)價(jià)指標(biāo)對(duì)提出的預(yù)測(cè)方法展開準(zhǔn)確性評(píng)價(jià),，基于 4 個(gè)案例 3 個(gè)指標(biāo)值的平均值分別為-0.38%,、5.23%和 0.82%。相比于現(xiàn)有主流機(jī)器學(xué)習(xí)方法,，提出的預(yù)測(cè)方法具有較高的預(yù)測(cè)精度,。

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關(guān)鍵詞 ：油田聯(lián)合站；水質(zhì)預(yù)測(cè),；機(jī)器學(xué)習(xí),；兩層分解算法,；改進(jìn)支持向量機(jī)

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 tradi

tional 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.

Key words： oilfield joint station; water quality prediction; machine learning; two-layer decomposition; modified support vector machine

收稿日期: 2021-09-29 ????

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通訊作者: [email protected]

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徐磊, 侯磊, 朱振宇, 徐震, 雷婷, 李雨, 李強(qiáng), 陳秀芹, 王九玲, 陳星燃. 基于兩層分解算法和改進(jìn)SVM的油田采出水處理效果預(yù)測(cè) 研究. 石油科學(xué)通報(bào), 2021, 03: 505-515 XU Lei, HOU Lei, ZHU Zhenyu, XU Zhen, LEI Ting, LI Yu, LI Qiang, CHEN Xiuqin, WANG Jiuling, CHEN Xingran. Prediction of oilfield produced water treatment based on a two-layer decomposition technique and modified SVM. Petroleum Science Bulletin, 2021, 03: 505-515.

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