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《Well Logging Technology》 2017-01
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Automatic Identification of Carbonate Sedimentary Facies Based on PCA and KNN Using Logs

LI Yanhua;WANG Hongtao;WANG Mingchuan;LIAN Peiqing;DUAN Taizhong;JI Bingyu;Exploration & Production Research Institute,SINOPEC;China National Oil and Gas Exploration & Development Corporation;  
In geological modeling and reserves calculation of carbonate reservoir,there is no uniform criterion for identification of sedimentary facies.The purpose of this paper is to study a set of automatic identification technology for carbonate sedimentary facies.Taking Y oilfield in the Middle East for example,principal component analysis(PCA)has first been adopted.The principal components are selected according to more than 90% cumulative variance contribution.At the same time,influence of oil on resistivity is removed by forward modeling,high frequency is cleared up by median filter and boundary is gained by mode filter.With core calibration,learning samples are established based on well logging data and sedimentary subfacies of core.Then K-nearest neighbor algorithm(KNN)is used to predict sedimentary subfacies of uncored wells.The result shows prediction accuracy of sedimentary subfacies is above 90%.By comparing other methods such as Artificial Neural Network(ANN)and Self Organizing Map(SOM),the technology is more suitable for a large number of learning samples and much classification overlap,and the prediction result is more reliable and stable.
【Fund】: 国家科技重大专项(2011ZX05031-003);; 科技部项目(G5800-15-ZS-KJB016)
【CateGory Index】: P618.13;P631.81
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