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). The applied CNN algorithm had an accuracy of 89.29%, a precision of 81.25% and a recall of 81.25%, and enabled the rapid (2 min) discrimination of pure EVOO that was adulterated with HO and HOSO in the volumetric ratio range of 10-100%. CONCLUSIONS: LF-NMR coupled with the CNN algorithm is a viable candidate for rapid EVOO authentication. © 2020 Society of Chemical Industry.

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 (T2), the longitudinal relaxation time (T1), and the diffusion coefficient correlation (D), three-dimensional (3D) NMR technique is superior with the complete measurement of T2, T1, 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. We developed a tri-window pulse sequence to collect the full decaying information of porous media. In the first window, the inversion-recovery pulse sequence is applied for T1 encoding. In the second window, D and T2 are encoded by an adjustable continuous pulse field gradient and echo spacing (TE). In the last window, CPMG with the shortest TE is used to acquire diffusion-free relaxation information. We then proposed a joint inversion algorithm named "composite-data-processing" to obtain the 3D correlation map. The algorithm adopts the dimension reduction technique and the truncated singular value decomposition (TSVD) to speed up the inversion process and enhance the inversion stability. Numerical simulations show that good estimations of the inversion results are obtained at different signal to noise ratios (SNRs). Our results suggest that the novel pulse sequence and inversion algorithm of 3D NMR can be effectively applied to the exploration of unconventional resources.

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. We divide the pore space into four intervals by the inflection points between the pore radius and the transversal relaxation time. Relationships between permeability and percentages of different pore intervals are investigated to investigate influential factors on the fluid transportation. Furthermore, an empirical model, which takes into account of the pore size distributions, is presented to compute the permeability. 212 core samples in our case show that the accuracy of permeability calculation is improved from 0.542 (SDR model), 0.507 (TIM model), 0.455 (conventional porosity-permeability regressions) to 0.803. To enhance the precision of downhole application of the new model, we developed a fluid correction algorithm to construct the water spectrum of in-situ NMR data, aiming to eliminate the influence of oil on the magnetization. The result reveals that permeability is positively correlated with percentages of mega-pores and macro-pores, but negatively correlated with the percentage of micro-pores. Poor correlation is observed between permeability and the percentage of meso-pores. NMR magnetizations and T2 spectrums after the fluid correction agree well with laboratory results for samples saturated with water. Field application indicates that the improved method provides better performance than conventional models such as Schlumberger-Doll Research equation, Timur-Coates equation, and porosity-permeability regressions.

An adaptive method for determining an acquisition parameter t0 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 t0 (the duration of the first window) is a key acquisition parameter. In order to obtain the optimal t0, an adaptive method is proposed in this paper. By studying the factors influencing discriminant factor σ and its variation trend using T2-G forward numerical simulation, it is found that the optimal t0 corresponds to the maximum value of σ. Then combining the constraint condition of SNR (Signal Noise Ratio) of spin echo, an optimal t0 in modified CPMG pulse sequence is determined. This method can reduce the difficulties of operating T2-G experiments. Finally, the adaptive method is verified by the results of the T2-G experiments for four water-saturated sandstone samples.

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. Then fast iterative shrinkage-thresholding algorithm (FISTA) is proposed to solve the L1 regularization problem. The effectiveness, noise vulnerability and practical utility of the proposed algorithm are analyzed by simulations and experiments. The results demonstrate that the proposed algorithm has a more excellent capability to reveal narrow peaks than traditional inversion algorithms. The L1 regularization implemented by our algorithm can be a useful complementary to the existing algorithms.

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. The fluid component decomposition (FCD) is applied to construct the forward T1-T2 model of the kerogen, and numerical simulations are conducted to investigate factors which may influence inversion results including echo spacing, recovery time series, signal to noise ratio (SNR), and the maximal iteration time. Results show that the T2 component is heavily impaired by the echo spacing, whereas the T1 component is influenced by the recovery time series but with limited effects. The inversion precision is greatly affected by the quality of the data. The inversed spectrum deviates from the model seriously when the SNR of the artificial noise is lower than 50, and the T2 component is more sensitive to the noise than the T1 component. What's more, the maximal iteration time can also affect the inversion result, especially when the maximal iteration time is smaller than 500. Proper acquisition and inversion parameters for the characterization of the kerogen are obtained considering the precision and the computational cost.

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. The wavelet basis function and decomposition level are optimized in consideration of information entropy and white noise estimation firstly. Then a hybrid threshold function is developed to avoid drawbacks of hard and soft threshold functions. To achieve the best thresholding values of different levels, a nonlinear objective function based on SNR and mean square error (MSE) is constructed, transforming the problem to a task of finding optimal solutions. Particle swarm optimization (PSO) is used to ensure the stability and global convergence. EWMA is carried out to eliminate unwanted peaks and sawtooths of the wavelet denoised signal. With validations of numerical simulations and experiments, it is demonstrated that the proposed approach can reduce the noise of T(2) decay data perfectly.