학술논문
一种基于BP神经网络的完井液污染类型识别方法 / A BP neural network-based identification method on the type of completion fluid contamination
Document Type
Academic Journal
Author
Source
石油与天然气化工 / Chemical Engineering of Oil and Gas. 52(6):117-123
Subject
Language
Chinese
ISSN
1007-3426
Abstract
目的 解决油田目的层钻井过程中完井液受盐水、残酸等污染后不能高效识别污染类型的问题.方法 对完井液进行不同质量占比的盐水、残酸污染测定,采用K-means聚类订正不同污染等级数据样本的标签.根据数据样本特征的获取难易度、隐藏层数目,训练不同的BP神经网络模型,并由留一交叉验证法检验模型的分类准确率.结果 数据样本拥有的特征越多,训练的 BP神经网络分类准确率越高,隐层数目越多,分类准确率反而越低.选择包含"流变+老化+滤失+井名"4 类特征的数据样本建立1 隐藏层的BP神经网络模型,其平均分类准确率达到 93.18%.结论 由流变、滤失等特征训练的 BP 神经网络模型可快速应用于试油现场,解决完井液污染类型识别问题,避免了试油现场因缺少大型仪器而无法鉴别完井液污染类型的难题.
Objective The aim is to solve the problem that the contamination type of the completion fluid can not be effectively identified after being contained by brine and residual acid during the drilling of the target layer.Methods The contamination of brine and residual acid with different mass fractions of the completion fluid was measured,and the labels of the data samples with different contamination degrees were revised by K-means clustering algorithm.Different BP neural network models were trained according to the difficulty of obtaining data sample features and the number of hidden layers,and the classification accuracy of the models was tested by leave-one-out cross validation method.Results It is found that the more features the data samples possess,the higher classification accuracy of the trained BP neural network could be achieved,while more hidden layers would lower the classification accuracy.The BP neural network model with one hidden layer was subsequently established with data samples that contain four kinds of features including"rheology+aging+filtration loss+well name".The average classification accuracy rate reached as high as 93.18%.Conclusions The BP neural network model trained by rheology and filtration loss features can be quickly deployed in the oil-testing sites to solve the problem of failing to identify the type of completion fluid contamination due to the lack of special equipment in the field.
Objective The aim is to solve the problem that the contamination type of the completion fluid can not be effectively identified after being contained by brine and residual acid during the drilling of the target layer.Methods The contamination of brine and residual acid with different mass fractions of the completion fluid was measured,and the labels of the data samples with different contamination degrees were revised by K-means clustering algorithm.Different BP neural network models were trained according to the difficulty of obtaining data sample features and the number of hidden layers,and the classification accuracy of the models was tested by leave-one-out cross validation method.Results It is found that the more features the data samples possess,the higher classification accuracy of the trained BP neural network could be achieved,while more hidden layers would lower the classification accuracy.The BP neural network model with one hidden layer was subsequently established with data samples that contain four kinds of features including"rheology+aging+filtration loss+well name".The average classification accuracy rate reached as high as 93.18%.Conclusions The BP neural network model trained by rheology and filtration loss features can be quickly deployed in the oil-testing sites to solve the problem of failing to identify the type of completion fluid contamination due to the lack of special equipment in the field.