测井技术2017，Vol.41Issue(1)：57-63,7.DOI:10.16489/j.issn.1004-1338.2017.01.010
基于PCA和KNN的碳酸盐岩沉积相测井自动识别
Automatic Identification of Carbonate Sedimentary Facies Based on PCA and KNN Using Logs
摘要
Abstract
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.关键词
测井评价/沉积相/碳酸盐岩/KNN算法/主成分分析/测井曲线Key words
log evaluation/sedimentary facies/carbonate rock/KNN/PCA/logging curve分类
天文与地球科学引用本文复制引用
李艳华,王红涛,王鸣川,廉培庆,段太忠,计秉玉..基于PCA和KNN的碳酸盐岩沉积相测井自动识别[J].测井技术,2017,41(1):57-63,7.基金项目
国家科技重大专项(2011ZX05031-003) （2011ZX05031-003）
科技部项目(G5800-15-ZS-KJB016) （G5800-15-ZS-KJB016）
