石油物探2017,Vol.56Issue(5):644-650,7.DOI:10.3969/j.issn.1000-1441.2017.05.004
基于低秩矩阵恢复的去噪方法在石油测井中的应用
A denoising method by low-rank matrix recovery and its application in oil well logging
摘要
Abstract
With the development of well logging techniques,the repository of data in the major oil fields has shown an enormous growth.Presence of redundancy and noise in well logging data requires the data to be compressed and denoised to make it useful for recognition of oil and gas layers.Low-rank matrix recovery (LRMR) theory generalizes the sparse representation of vector samples in compressed sensing (CS) to the matrix of low rank case.This theory considers recovery of the low-rank data matrix from large and sparse errors,leading to better maintenance of data structure and achieving a superior denoising effect.Thus,here we propose a denoising method through low-rank matrix recovery,and application of its three algorithms (accelerated proximal gradient (APG) algorithm,exact augmented Lagrange multiplier (EALM),and inexact augmented Lagrange multiplier (IALM)) to oil well log ging data to improve the denoising effect.Pre-and post-denoising logging data were consequently used in oil and gas layer recognition by support vector machine (SVM) and relevance vector machine (RVM),respectively.Results show that oil and gas layer recognition accuracy is improved remarkably by the three denoising algorithms,compared to when denoising was not applied.IALM algorithm was superior to EALM and APG algorithms,through parameter optimization to reduce the number of iterations,which could obviously improve the operation efficiency.关键词
石油测井/数据去噪/低秩矩阵恢复/加速近端梯度算法/增广拉格朗日乘子法Key words
oil well logging/data de-noising/low rank matrix recovery/accelerated proximal gradient (APG) algorithm/augmented Lagrange multiplier (EALM) algorithm分类
天文与地球科学引用本文复制引用
王艳伟,夏克文,牛文佳,Ali Ahamd..基于低秩矩阵恢复的去噪方法在石油测井中的应用[J].石油物探,2017,56(5):644-650,7.基金项目
河北省自然科学基金(E2016202341)资助.This research is financially supported by Hebei Province Natural Science Foundation (Grant No.E2016202341). (E2016202341)