Characterization of the pore structure of tight sandstone reservoirs carries great significance for the evaluation of the reservoir storage and transport properties, “sweet spots” prediction, and reservoir development. In this paper, the pore structures of tight sandstone reservoirs are characterized on the basis of the multifractal analysis of nuclear magnetic resonance (NMR) transverse relaxation time (T 2) distributions. For the T 2 distribution models established using the mixed Gaussian distribution function, the characteristic parameters of the high probability measure areas (the right branch of the generalized fractal dimension spectrum and the left branch of the singularity spectrum) are closely related to the model parameters (weight coefficients and standard deviations of the short relaxation component). For the T 2 distributions of the tight sandstone samples, the results of the relationships between the multifractal characteristic parameters of the T 2 distributions and the petrophysical parameters of the samples indicate that the characteristic parameters of the high probability measure areas (α max , α0 – α max , D max , and D 0 – D max ) are closely related to the permeability, T 2 geometric mean (T 2lm ), and T 35 (the T 2 value at 35% saturation in the normalized reverse accumulated T 2 distribution curve) values, which can be used to quantitatively evaluate the pore structure heterogeneity of tight sandstones. These samples can be classified into four types on the basis of the shape of the T 2 distribution and the petrophysical parameters. Substantial differences exist among the multifractal characteristics of the different sample types, and several parameters (α max , α0 – α max , D max , and D 0 – D max ) can be used to classify the sample types. The multifractal characteristic parameters are closely related to the clay mineral content. As the clay mineral content increases, the heterogeneity of the high probability measure areas of the T 2 distribution increases. The results of the multifractal analysis of the NMR logging data further demonstrate the effectiveness of evaluating the pore structure of tight sandstone reservoirs based on the multifractal characteristics of the NMR T 2 distribution.

No abstract

No abstract

No abstract

Permeability and bound water saturation (Swb) are key parameters reflecting the petrophysical properties of porous rocks. Nuclear magnetic resonance (NMR) has proved to be effective in investigating the properties of porous media. However, estimating Swb and the permeability in tight sandstone reservoirs based on conventional NMR methods, which requires the inversion of NMR echo data to obtain the transverse relaxation time (T2) distribution, proves to be a challenging task. In this study, a method is proposed to estimate Swb and the permeability in tight sandstone reservoirs based on the direct analysis of NMR echo data, thus avoiding the inversion process. A total of 20 tight sandstone samples from the Ordos Basin in China was taken for laboratory NMR measurements. The kernel function of the echo data was used to characterize the ratio distribution of bound water volume to pore volume for different pore sizes. Following this, an echo data calibration method was applied to estimate Swb without the inversion of the T2 distribution, and the window method was used to reduce the impact of noise in the echo data. Furthermore, models for estimating permeability were proposed based on the determined windows of the NMR echo data in the Swb estimation. The reliability of the proposed method for estimating Swb and permeability was verified by comparing the estimated and experimental results. Our study provides an efficient method for the estimation of petrophysical parameters in porous rocks based on the direct analysis of NMR echo data.

The purpose of this research is to use the image recognition technology of smart sensors to establish a preschool education system for preschool children, which can realize interactive image-based learning and education for preschool children. This paper uses CMOS image sensor (IV2-6500CA of KEYENCE) to design the front-end image acquisition hardware system and establishes an image recognition system that can realize most of the recognition functions based on BP neural network algorithm and ImageNet, MS COCO, MNIST, and the Chars74K dataset and integrate the image recognition system into the preschool education interactive system. The image recognition system has a high accuracy rate, with an overall accuracy rate of 85.16%. Compared with the traditional preschool education system, it has a higher recognition rate, better teaching efficiency, and interactivity. It can recognize most of the objects that children touch and have a good interactive education effect.

Nuclear magnetic resonance (NMR) technology plays a significant role in petroleum exploration. NMR data can be processed using inversion methods to reflect the relaxation information of all the components. We have developed a new double-parameter regularization (DPR) method for the inversion of NMR data, whose regularization terms consist of Tikhonov regularization and maximum entropy regularization. The objective function for the DPR method was solved using the Levenberg-Marquardt method, the proportional coefficient of the regularization parameter was obtained using an iteration procedure, and the optimum regularization parameter of the DPR method was selected using an S-curve. The relationship between the optimum regularization parameter and the signal-to-noise ratio (S/N) of the data was evaluated. Moreover, we compared the results of the NMR inversion obtained from the norm smooth method, the maximum entropy method, and the DPR method for simulated data. We evaluated how the proportional coefficient of the regularization parameter affected the inverted [Formula: see text] distributions and processed field NMR log data for a tight sandstone reservoir using the DPR method. The results indicated that the optimum regularization parameter for the DPR method gradually decreases with increasing data S/N. The accuracy is higher for the DPR method than for the norm smooth method and the maximum entropy method under low-S/N conditions. It is of great importance to select the proportional coefficient for the DPR method. The inverted [Formula: see text] distributions are similar for the DPR method and the norm smooth method when the proportional coefficient is small, and this is similar for the DPR method and the maximum entropy method when the proportional coefficient is large.

No abstract