Nuclear magnetic resonance (NMR) is a sophisticated technology to gain insights into the Earth's physical and chemical properties, such as porosity, permeability, fluid viscosity, and pore structure in near-surface environments. The signal-to-noise ratio (SNR) is one of the most critical challenges in applying NMR to reservoir pore media, as the data acquired from NMR instruments must be inverted into NMR relaxation spectrum to estimate formation information, where the inversion is an inherently ill-posed problem. In particular, the target of NMR detection is transitioning to the ultra-deep reservoirs, which are characterized by an extremely low porosity. In these environments, the NMR data typically exhibit very low SNR due to the limited fluid volume within the sensitive region and harsh measurement conditions, both of which significantly impact the quality of the inverted spectra. Therefore, enhancing SNR prior to spectrum inversion, i.e., through data denoising, is essential.

This paper reviews methods for denoising NMR echo data, including mathematical transformation methods, morphological filtering techniques, and artificial intelligence (AI)-based methods. Their advantages and disadvantages of each method were compared and analyzed. The development trend in NMR data denoising is summarized. A multi-dimensional denoising strategy that integrates mathematical transformation and AI technologies, along with the development of lightweight AI models, shows great promise for NMR echo data denoising.

Continental shale oil reservoirs in China, particularly those of low to medium maturity, contain a high proportion of untransformed solid organic matter (SOM). The SOM plays a critical role as a potential oil and gas resource. Nuclear magnetic resonance (NMR) is a powerful technique for the evaluation of shale oil reservoirs. However, it is challenging for conventional T₁-T₂ measurement methods to fully capture signals from ultra-short relaxation components such as SOM, due to the measurement deficiency caused by NMR instruments. To this end, the free induction decay (FID) and inversion recovery FID (IR-FID) pulse sequences are introduced, and two novel methods are proposed for quantitative characterization of SOM. The first method, Method Ⅰ, employs the signal amplitude difference between T₂^∗ and T₁-T₂ spectra to obtain the SOM content. The second, Method Ⅱ, directly quantifies the SOM signal from the T₁-T₂^∗ spectrum. A novel parameter, the ratio of T₁/T₂^∗ to T₁/T₂, is also proposed to refine the identification of SOM in the T₁-T₂ spectrum. The effectiveness of the proposed methods is validated by strong correlations with four geochemical parameters indicative of SOM content. The results from Method Ⅰ show significantly improved correlations with all four geochemical parameters compared to the conventional T₁-T₂ method. The results from Method Ⅱ show excellent correlations with parameters from step-by-step (SBS) Rock-Eval pyrolysis, reaching coefficients of determination (R²) as high as 0.8958 and 0.8828. This method also shows strong numerical consistency with the geochemical parameters, specifically with (S_1–2b + S_2-1+S_2-2). Method Ⅱ is therefore highly suitable for quantitatively evaluating the total solid hydrogen content, including solid petroleum hydrocarbons, bitumen, and kerogen. This work achieves, for the first time, the precise quantification of SOM at the core scale, providing a high-precision, large-scale, and non-destructive approach for evaluating the resource potential of shale oil reservoirs.

It is of great significance to evaluate the petrophysical properties in shale oil reservoir, which can contribute to geological storage CO₂. Two-dimensional nuclear magnetic resonance (2D NMR) technology has been applied to petrophysical characterization in shale oil reservoir. However, limitations of traditional 2D NMR (T₁-T₂ or T₂-D) in detecting short-lived organic matter and the complexity of mineral compositions, pose NMR-based petrophysical challenges. The organic pores were assumed saturated oil and the inorganic pores were assumed saturated water, and the numerical algorithm and theory of T₁-T₂* in shale oil reservoir were proposed, whose accuracy was validated through T_2, T₁-T₂ and T₂* experiments. The effects of mineral types and contents on the T₁-T₂* responses were firstly simulated by the random walk algorithm, revealing the NMR response mechanisms in shale oil reservoir with complex mineral compositions at different magnetic field frequency (f). The results indicate that when the pyrite content is 5.43%, dwell time is 4 μs, the f is 200 MHz, and echo spacing is 0.4 ms, the T₁-T₂*-based porosity is 2.39 times that of T₁-T₂-based porosity. The T_2LM* is 0.015 ms, which is 0.015 times that of T_2LM. The T_1LM is 8.84 ms, which is 0.63 times that of T_1LM. The T₁-T₂*-based petrophysical conversion models were firstly created, and the foundation of petrophysical conversion was laid at different f.

Characterizing the petrophysical properties holds significant importance in shale oil reservoirs. Two-dimensional (2-D) nuclear magnetic resonance (NMR), a nondestructive and noninvasive technique, has numerous applications in petrophysical characterization. However, the complex occurrence states of the fluids and the highly non-uniform distributions of minerals and organic matter pose challenges in the NMR-based petrophysical characterization. A novel T₁-T₂ relaxation theory is introduced for the first time in this study. The transverse and longitudinal relaxivities of pore fluids are determined based on numerical investigation and experimental analysis. Additionally, an improved random walk algorithm is proposed to, on the basis of digital shale core, simulate the effects of the hydrogen index (HI) for the organic matter, echo spacing (T_E), pyrite content, clay mineral type, and clay content on T₁-T₂ spectra at different NMR frequencies. Furthermore, the frequency conversion cross-plots for various petrophysical parameters influenced by the above factors are established. This study provides new insights into NMR-based petrophysical characterization and the frequency conversion of petrophysical parameters measured by laboratory NMR instruments and NMR logging in shale oil reservoirs. It is of great significance for the efficient exploration and environmentally friendly production of shale oil.

In view of the characteristics of nuclear magnetic resonance (NMR) logging teaching with strong theory content and abstract and complex principles, this paper designs a reservoir fluid identification simulation experiment of nuclear magnetic resonance (NMR) dual waiting time (TW) logging based on MATLAB GUI. The designed MATLAB GUI interactive interface includes the construction of formation T₂ spectrum model, fluid polarization, dual TW echo data forward modeling and echo data processing modules. By entering different parameters in the simulation experiment, the simulated formation T₂ spectrum model, long and short TW echo data and processing results can be displayed in real time in the graphical interface, and the actual NMR logging data are processed. The simulation experiment is simple to operate, which can visually display the reservoir fluid identification process and results of NMR dual TW logging to students, enhance students' understanding of the fluid identification method, and improve the teaching effect of NMR logging.

The resistivity difference between oil and gas layers and the water layers in low contrast tight sandstone reservoirs is subtle. Fluid identification and saturation calculation based on conventional logging methods are facing challenges in such reservoirs. In this paper, a new method is proposed for fluid identification and saturation calculation in low contrast tight sandstone reservoirs. First, a model for calculating apparent formation water resistivity is constructed, which takes into account the influence of shale on the resistivity calculation and avoids apparent formation water resistivity abnormal values. Based on the distribution of the apparent formation water resistivity obtained by the new model, the water spectrum is determined for fluid identification in low contrast tight sandstone reservoirs. Following this, according to the average, standard deviation, and endpoints of the water spectrum, a new four-parameter model for calculating reservoir oil and gas saturation is built. The methods proposed in this paper are applied to the low contrast tight sandstone reservoirs in the Q4 formation of the X53 block and X70 block in the south of Songliao Basin, China. The results show that the water spectrum method can effectively distinguish oil-water layers and water layers in the study area. The standard deviation of the water spectrum in the oil-water layer is generally greater than that in the water layer. The new four-parameter model yields more accurate oil and gas saturation. These findings verify the effectiveness of the proposed methods.

In this research, an integrated classification method based on principal component analysis– simulated annealing genetic algorithm–fuzzy cluster means (PCA–SAGA–FCM) was proposed for the unsupervised classification of tight sandstone reservoirs which lack the prior information and core experiments. A variety of evaluation parameters were selected, including lithology characteristic parameters, poro-permeability quality characteristic parameters, engineering quality characteristic parameters, and pore structure characteristic parameters. The PCA was used to reduce the dimension of the evaluation parameters, and the low-dimensional data was used as input. The unsupervised reservoir classification of tight sandstone reservoir was carried out by the SAGA-FCM, the characteristics of reservoir at different categories were analyzed and compared with the lithological profiles. The analysis results of numerical simulation and actual logging data show that: 1) compared with FCM algorithm, SAGA–FCM has stronger stability and higher accuracy; 2) the proposed method can cluster the reservoir flexibly and effectively according to the degree of membership; 3) the results of reservoir integrated classification match well with the lithologic profile, which demonstrates the reliability of the classification method.