A potential NMR-based wettability index using free induction decay for rocks

The wettability of reservoir rocks saturated with oil and water is one of the most important factors influencing petrophysics and oil recovery.Minerals with different wettability constitute the overall heterogeneous wettability in rocks.Variations in sample composi-tion can be detected by nuclear magnetic resonance(NMR)measurements.In this paper,the method of using the magnetic susceptibility contrast between rock skeleton and saturated fluid to estimate wettability is proposed.The theoretical feasibility was firstly analyzed,and then the internal gradients caused by magnetic susceptibility contrasts were employed to interpret wettability alteration before and after ageing process in rocks.It was discovered that water and oil in the same pores experienced different internal gradients after ageing,which were associated with the differences in magnetic susceptibility con-trasts.After that,the free induction decay measurement was performed to acquire mag-netic susceptibility contrasts of artificial sandstone samples with the intermediate-wet condition.A refined NMR wettability index was presented and correlated with the Amott wettability tests.The experimental results demonstrate that the new method for deter-mining wettability is feasible.

A machine learning framework for low-field NMR data processing

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.

Low-field NMR inversion based on low-rank and sparsity restraint of relaxation spectra

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.

钙视网膜蛋白和乙酰胆碱酯酶在新生儿先天性巨结肠症直肠黏膜活检病理诊断中的应用

目的 探讨钙视网膜蛋白（calretinin,CR）和乙酰胆碱酯酶（acetylcholinesterase,AchE）在新生儿先天性巨结肠症（Hirschsprung＇s disease,HSCR）直肠黏膜活检病理诊断中的应用价值.方法 收集2015年1月至2016年1月新生儿直肠黏膜活检标本62例.此62例患儿均有出生后反复出现腹胀、便秘、胎便延迟和/或呕吐等症状,临床诊断或疑诊HSCR.其中,男47例,女15例;年龄为3～28 d,平均17 d.在显微镜下观察黏膜下神经节细胞和神经纤维直径,免疫组织化学标记CR和酶组织化学染色AchE.根据直肠黏膜活检标本黏膜下HE染色有、无神经节细胞将患儿分为非HSCR组和HSCR组,比较两组CR和AchE的表达.结果 HSCR组（49例）无髓鞘神经纤维直径在30～70μm,平均49.6μm;其中直径≥40μm 42例（85.7％）,〈40μm 7例（14.3％,30～38μm）.CR无阳性表达患儿.AchE阳性45例（91.8％）,阴性4例（8.2％）.非HSCR组（13例）无髓鞘神经纤维直径〈25μm者12例（92.3％）;神经纤维直径35μm者1例（7.7％）.CR阳性13例（100％）;AchE阳性1例（7.7％）,阴性12例（92.3％）.CR表达在HSCR诊断中的敏感性和特异性为100％（12/12）,98.0％（49/50）;阳性预测值为92.3％（12/13）,阴性预测值为100％（49/49）.AchE在HSCR诊断中的敏感性和特异性分别为93.9％（46/49）和100％（12/12）,阳性预测值100％ 为（46/46）,阴性预测值为75.0％（12/16）.结论 直肠黏膜活检HE染色结合CR和AchE的病理诊断,有助于提高新生儿HSCR病理诊断准确率,具有较好的应用价值.