Rapid screening for hazelnut oil and high-oleic sunflower oil in extra virgin olive oil using low-field nuclear magnetic resonance relaxometry and machine learning.

Background: As extra virgin olive oil (EVOO) has high commercial value, it is routinely adulterated with other oils. The present study investigated the feasibility of rapidly identifying adulterated EVOO using low-field nuclear magnetic resonance (LF-NMR) relaxometry and machine learning approaches (decision tree, K-nearest neighbor, linear discriminant analysis, support vector machines and convolutional neural network (CNN)).

Results: LF-NMR spectroscopy effectively distinguished pure EVOO from that which was adulterated with hazelnut oil (HO) and high-oleic sunflower oil (HOSO).

A novel pulse sequence and inversion algorithm of three-dimensional low field NMR technique in unconventional resources.

Compared with two-dimensional (2D) nuclear magnetic resonance (NMR) technique like correlations among the transversal relaxation time (T), the longitudinal relaxation time (T), and the diffusion coefficient correlation (D), three-dimensional (3D) NMR technique is superior with the complete measurement of T, T, and D simultaneously. It can solve the problem of overlaps in 2D correlation map and is helpful to characterize relaxation components in unconventional resources such as tight gas and oil shale. However, the existed 3D NMR technique is restricted due to the loss of short relaxation information and the inversion inaccuracy that caused by the incomplete measurement of the diffusion editing window.

An improved method for permeability estimation of the bioclastic limestone reservoir based on NMR data.

Permeability is an important parameter in formation evaluation since it controls the fluid transportation of porous rocks. However, it is challengeable to compute the permeability of bioclastic limestone reservoirs by conventional methods linking petrophysical and geophysical data, due to the complex pore distributions. A new method is presented to estimate the permeability based on laboratory and downhole nuclear magnetic resonance (NMR) measurements.

An adaptive method for determining an acquisition parameter t in a modified CPMG sequence.

The modified CPMG (Carr-Purcell-Meiboom-Gill) pulse sequence is a common sequence used for measuring the internal magnetic field gradient distribution of formation rocks, for which t (the duration of the first window) is a key acquisition parameter. In order to obtain the optimal t, an adaptive method is proposed in this paper. By studying the factors influencing discriminant factor σ and its variation trend using T-G forward numerical simulation, it is found that the optimal t corresponds to the maximum value of σ.

The inversion of 2D NMR relaxometry data using L1 regularization.

NMR relaxometry has been used as a powerful tool to study molecular dynamics. Many algorithms have been developed for the inversion of 2D NMR relaxometry data. Unlike traditional algorithms implementing L2 regularization, high order Tikhonov regularization or iterative regularization, L1 penalty term is involved to constrain the sparsity of resultant spectra in this paper.

Probing the influential factors of NMR T1-T2 spectra in the characterization of the kerogen by numerical simulation.

The low field nuclear magnetic resonance (NMR) spectroscopy has been widely used to characterize the longitudinal and transversal relaxation (T1-T2) spectrum of unconventional resources such as shale gas and tight oil containing significant proportions of kerogen and bitumen. However, it requires exquisite design of the acquisition model and the inversion algorithm due to the fast relaxation nature of the kerogen and bitumen. A new direct two dimensional (2D) inversion algorithm combined the iterative truncated singular value decomposition (TSVD) and the Akaiake Information Criterion (AIC) is presented to perform the data inversion efficiently.

Noise reduction of nuclear magnetic resonance (NMR) transversal data using improved wavelet transform and exponentially weighted moving average (EWMA).

NMR logging and core NMR signals acts as an effective way of pore structure evaluation and fluid discrimination, but it is greatly contaminated by noise for samples with low magnetic resonance intensity. Transversal relaxation time (T(2)) spectrum obtained by inversion of decay signals intrigued by Carr-Purcell-Meiboom-Gill (CPMG) sequence may deviate from the truth if the signal-to-noise ratio (SNR) is imperfect. A method of combing the improved wavelet thresholding with the EWMA is proposed for noise reduction of decay data.