Low-field(nuclear magnetic resonance)NMR has been widely used in petroleum industry,such as well logging and laboratory rock core analysis.However,the signal-to-noise ratio is low due to the low magnetic field strengt...Low-field(nuclear magnetic resonance)NMR has been widely used in petroleum industry,such as well logging and laboratory rock core analysis.However,the signal-to-noise ratio is low due to the low magnetic field strength of NMR tools and the complex petrophysical properties of detected samples.Suppressing the noise and highlighting the available NMR signals is very important for subsequent data processing.Most denoising methods are normally based on fixed mathematical transformation or handdesign feature selectors to suppress noise characteristics,which may not perform well because of their non-adaptive performance to different noisy signals.In this paper,we proposed a“data processing framework”to improve the quality of low field NMR echo data based on dictionary learning.Dictionary learning is a machine learning method based on redundancy and sparse representation theory.Available information in noisy NMR echo data can be adaptively extracted and reconstructed by dictionary learning.The advantages and application effectiveness of the proposed method were verified with a number of numerical simulations,NMR core data analyses,and NMR logging data processing.The results show that dictionary learning can significantly improve the quality of NMR echo data with high noise level and effectively improve the accuracy and reliability of inversion results.展开更多
In this paper,we proposed a novel method for low-field nuclear magnetic resonance(NMR)inversion based on low-rank and sparsity restraint(LRSR)of relaxation spectra,with which high quality construction is made possible...In this paper,we proposed a novel method for low-field nuclear magnetic resonance(NMR)inversion based on low-rank and sparsity restraint(LRSR)of relaxation spectra,with which high quality construction is made possible for one-and two-dimensional low-field and low signal to noise ratio NMR data.In this method,the low-rank and sparsity restraints are introduced into the objective function instead of the smoothing term.The low-rank features in relaxation spectra are extracted to ensure the local characteristics and morphology of spectra.The sparsity and residual term are contributed to the resolution and precision of spectra,with the elimination of the redundant relaxation components.Optimization process of the objective function is designed with alternating direction method of multiples,in which the objective function is decomposed into three subproblems to be independently solved.The optimum solution can be obtained by alternating iteration and updating process.At first,numerical simulations are conducted on synthetic echo data with different signal-to-noise ratios,to optimize the desirable regularization parameters and verify the feasibility and effectiveness of proposed method.Then,NMR experiments on solutions and artificial sandstone samples are conducted and analyzed,which validates the robustness and reliability of the proposed method.The results from simulations and experiments have demonstrated that the suggested method has unique advantages for improving the resolution of relaxation spectra and enhancing the ability of fluid quantitative identification.展开更多
D-T_(2)two-dimensional nuclear magnetic resonance(2D NMR)logging technology can distinguish pore fluid types intuitively,and it is widely used in oil and gas exploration.Many 2D NMR inversion methods(e.g.,truncated si...D-T_(2)two-dimensional nuclear magnetic resonance(2D NMR)logging technology can distinguish pore fluid types intuitively,and it is widely used in oil and gas exploration.Many 2D NMR inversion methods(e.g.,truncated singular value decomposition(TSVD),Butler-Reds-Dawson(BRD),LM-norm smoothing,and TIST-L1 regularization methods)have been proposed successively,but most are limited to numerical simulations.This study focused on the applicability of different inversion methods for NMR logging data of various acquisition sequences,from which the optimal inversion method was selected based on the comparative analysis.First,the two-dimensional NMR logging principle was studied.Then,these inversion methods were studied in detail,and the precision and computational efficiency of CPMG and diffusion editing(DE)sequences obtained from oil-water and gas-water models were compared,respectively.The inversion results and calculation time of truncated singular value decomposition(TSVD),Butler-Reds-Dawson(BRD),LM-norm smoothing,and TIST-L1 regularization were compared and analyzed through numerical simulations.The inversion method was optimized to process SP mode logging data from the MR Scanner instrument.The results showed that the TIST-regularization and LM-norm smoothing methods were more accurate for the CPMG and DE sequence echo trains of the oil-water and gas-water models.However,the LM-norm smoothing method was less time-consuming,making it more suitable for logging data processing.A case study in well A25 showed that the processing results by the LM-norm smoothing method were consistent with GEOLOG software.This demonstrates that the LM-norm smoothing method is applicable in practical NMR logging processing.展开更多
Bruker TopSpinTM核磁共振软件中集成的DOSY数据采集和处理模块DOSYmTM,可以方便简单地进行DOSY实验的在线数据采集与处理,并已很好地应用于复杂混合物的定性和定量分析,如天然混合物、石油产品、医药产品、食品工业及质量控制等领域....Bruker TopSpinTM核磁共振软件中集成的DOSY数据采集和处理模块DOSYmTM,可以方便简单地进行DOSY实验的在线数据采集与处理,并已很好地应用于复杂混合物的定性和定量分析,如天然混合物、石油产品、医药产品、食品工业及质量控制等领域.本文介绍了DOSY实验的原理,数据采集和处理的方法及DOSYmTM模块的应用.展开更多
基金supported by Science Foundation of China University of Petroleum,Beijing(Grant Number ZX20210024)Chinese Postdoctoral Science Foundation(Grant Number 2021M700172)+1 种基金The Strategic Cooperation Technology Projects of CNPC and CUP(Grant Number ZLZX2020-03)National Natural Science Foundation of China(Grant Number 42004105)
文摘Low-field(nuclear magnetic resonance)NMR has been widely used in petroleum industry,such as well logging and laboratory rock core analysis.However,the signal-to-noise ratio is low due to the low magnetic field strength of NMR tools and the complex petrophysical properties of detected samples.Suppressing the noise and highlighting the available NMR signals is very important for subsequent data processing.Most denoising methods are normally based on fixed mathematical transformation or handdesign feature selectors to suppress noise characteristics,which may not perform well because of their non-adaptive performance to different noisy signals.In this paper,we proposed a“data processing framework”to improve the quality of low field NMR echo data based on dictionary learning.Dictionary learning is a machine learning method based on redundancy and sparse representation theory.Available information in noisy NMR echo data can be adaptively extracted and reconstructed by dictionary learning.The advantages and application effectiveness of the proposed method were verified with a number of numerical simulations,NMR core data analyses,and NMR logging data processing.The results show that dictionary learning can significantly improve the quality of NMR echo data with high noise level and effectively improve the accuracy and reliability of inversion results.
基金supported by “National Natural Science Foundation of China (Grant No. 42204106)”“China Postdoctoral Science Foundation (Grant No. 2021M700172)”+1 种基金“The Strategic Cooperation Technology Projects of CNPC and CUP (Grant No. ZLZX2020-03)”“Natural Science Foundation of the Jiangsu Higher Education Institutions of China (Grant No. 20KJD430002)”
文摘In this paper,we proposed a novel method for low-field nuclear magnetic resonance(NMR)inversion based on low-rank and sparsity restraint(LRSR)of relaxation spectra,with which high quality construction is made possible for one-and two-dimensional low-field and low signal to noise ratio NMR data.In this method,the low-rank and sparsity restraints are introduced into the objective function instead of the smoothing term.The low-rank features in relaxation spectra are extracted to ensure the local characteristics and morphology of spectra.The sparsity and residual term are contributed to the resolution and precision of spectra,with the elimination of the redundant relaxation components.Optimization process of the objective function is designed with alternating direction method of multiples,in which the objective function is decomposed into three subproblems to be independently solved.The optimum solution can be obtained by alternating iteration and updating process.At first,numerical simulations are conducted on synthetic echo data with different signal-to-noise ratios,to optimize the desirable regularization parameters and verify the feasibility and effectiveness of proposed method.Then,NMR experiments on solutions and artificial sandstone samples are conducted and analyzed,which validates the robustness and reliability of the proposed method.The results from simulations and experiments have demonstrated that the suggested method has unique advantages for improving the resolution of relaxation spectra and enhancing the ability of fluid quantitative identification.
基金sponsored by the National Natural Science Foundation of China(Nos.42174149,41774144)the National Major Projects(No.2016ZX05014-001).
文摘D-T_(2)two-dimensional nuclear magnetic resonance(2D NMR)logging technology can distinguish pore fluid types intuitively,and it is widely used in oil and gas exploration.Many 2D NMR inversion methods(e.g.,truncated singular value decomposition(TSVD),Butler-Reds-Dawson(BRD),LM-norm smoothing,and TIST-L1 regularization methods)have been proposed successively,but most are limited to numerical simulations.This study focused on the applicability of different inversion methods for NMR logging data of various acquisition sequences,from which the optimal inversion method was selected based on the comparative analysis.First,the two-dimensional NMR logging principle was studied.Then,these inversion methods were studied in detail,and the precision and computational efficiency of CPMG and diffusion editing(DE)sequences obtained from oil-water and gas-water models were compared,respectively.The inversion results and calculation time of truncated singular value decomposition(TSVD),Butler-Reds-Dawson(BRD),LM-norm smoothing,and TIST-L1 regularization were compared and analyzed through numerical simulations.The inversion method was optimized to process SP mode logging data from the MR Scanner instrument.The results showed that the TIST-regularization and LM-norm smoothing methods were more accurate for the CPMG and DE sequence echo trains of the oil-water and gas-water models.However,the LM-norm smoothing method was less time-consuming,making it more suitable for logging data processing.A case study in well A25 showed that the processing results by the LM-norm smoothing method were consistent with GEOLOG software.This demonstrates that the LM-norm smoothing method is applicable in practical NMR logging processing.
