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
The performance of a biofilter for off-gas treatment relies on the activity of microorganisms and adequate O_2 and H_2O. In present study, a microelectrode was applied to analyze O_2 in polyurethane foam cubes（PUFCs...The performance of a biofilter for off-gas treatment relies on the activity of microorganisms and adequate O_2 and H_2O. In present study, a microelectrode was applied to analyze O_2 in polyurethane foam cubes（PUFCs） packed in a biofilter for SO_2 removal. The O_2 distribution varied with the density and water-containing rate（WCR） of PUFCs. The O_2 concentration dropped sharply from 10.2 to 0.8 mg/L from the surface to the center of a PUFC with 97.20%of WCR. The PUFCs with high WCR presented aerobic–anoxic–aerobic areas.Three-dimensional simulated images demonstrated that the structure of PUFCs with high WCR consisted of an aerobic ＂shell＂ and an anoxic ＂core＂, with high-density PUFCs featuring a larger anoxic area than low-density PUFCs. Moreover, the H_2O distribution in the PUFC was uneven and affected the O_2 concentration. Whereas aerobic bacteria were observed in the PUFC surface, facultative anaerobic microorganisms were found at the PUFC core, where the O_2 concentration was relatively low. O_2 and H_2O distributions differed in the PUFCs, and the distribution of microorganisms varied accordingly.展开更多
在相同的条件下,用电子探针测量了经 NaNO_3盐浴处理的玻璃中和原基体玻璃中钠的特征 X 射线强度分布;依据基体玻璃中已知 Na_2O 的浓度和对应钠的特征 X 射线强度作出浓度标定曲线,进而得到离子交换的玻璃中 Na_2O 浓度分布曲线;并讨...在相同的条件下,用电子探针测量了经 NaNO_3盐浴处理的玻璃中和原基体玻璃中钠的特征 X 射线强度分布;依据基体玻璃中已知 Na_2O 的浓度和对应钠的特征 X 射线强度作出浓度标定曲线,进而得到离子交换的玻璃中 Na_2O 浓度分布曲线;并讨论了定量分析结果的误差。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financially supported by the Major Science and Technology Program for Water Pollution Control and Treatment(No.2010ZX07319-001-03)the National Natural Science Foundation of China(No.51221892)
文摘The performance of a biofilter for off-gas treatment relies on the activity of microorganisms and adequate O_2 and H_2O. In present study, a microelectrode was applied to analyze O_2 in polyurethane foam cubes（PUFCs） packed in a biofilter for SO_2 removal. The O_2 distribution varied with the density and water-containing rate（WCR） of PUFCs. The O_2 concentration dropped sharply from 10.2 to 0.8 mg/L from the surface to the center of a PUFC with 97.20%of WCR. The PUFCs with high WCR presented aerobic–anoxic–aerobic areas.Three-dimensional simulated images demonstrated that the structure of PUFCs with high WCR consisted of an aerobic ＂shell＂ and an anoxic ＂core＂, with high-density PUFCs featuring a larger anoxic area than low-density PUFCs. Moreover, the H_2O distribution in the PUFC was uneven and affected the O_2 concentration. Whereas aerobic bacteria were observed in the PUFC surface, facultative anaerobic microorganisms were found at the PUFC core, where the O_2 concentration was relatively low. O_2 and H_2O distributions differed in the PUFCs, and the distribution of microorganisms varied accordingly.
