Key laboratory of Exploration Technologies for Oil and Gas ResourcesYangtze University,Ministry of Education,Wuhan
研究区块低阻油层发育广泛,油层和水层的电阻率相差不大,导致测井流体识别较为困难。为了有效识别低阻油层,本文采用Smote过采样算法对油水同层,油层等少数类样本进行过采样使数据集均衡；并利用门控循环单元(gated recurrent unit,GRU)网络模型进行低阻油层的流体识别。通过相关性分析确定了GR,RD,DEN等8条测井曲线数据作为输入训练模型,应用于中实际资料中,并将GRU与传统RNN和其他3种机器学习算法对比。结果表明,序列数据模型的流体识别效果比传统机器学习模型好,且基于Smote-GRU的流体识别模型的符合率达到89.5%(相对传统RNN的81.1%),取得了较好的应用效果。通过对照试验还证实了Smote算法提高了分类器对少数类样本的识别率。本文提出的方法可为样本不均衡的低阻油层的流体识别提供参考。
The development of low resistivity oil layers in the research block is extensive, and the difference in resistivity between oil layers and water layers is not significant, making it difficult to identify logging fluids. This paper uses Smote Oversampling algorithm to Oversampling a few samples of oil and water in the same layer, oil layers and so on to balance the data set. And uses gated recurrent unit (GRU) network model for fluid identification of low resistivity oil layers. Through correlation analysis, eight logging curve data such as GR, RD and DEN are determined as input training models, which are applied in actual data. GRU is compared with traditional RNN and other three machine learning algorithms. The results show that the fluid identification performance of sequential data models is better than that of traditional machine learning models and the coincidence rate of the fluid recognition model based on Smote GRU reaches 89.5% (compared to 81.1% of traditional RNN), achieving good application results. Through comparative experiments, it was also confirmed that the Smote algorithm improved the classifier"s recognition rate for minority class samples. The method proposed in this article can provide reference for fluid identification of low resistivity oil layers with imbalanced samples.
龚宇,刘迪仁. 基于门控循环单元网络的低阻油层测井流体识别方法[J]. 科学技术与工程, , ():复制