Capillary pressure curves are usually obtained through mercury injection experiments, which are mainly used to characterize pore structures. However, mercury injection experiments have many limitations, such as operation danger, a long experiment period, and great damage to the sample. Therefore, researchers have tried to predict capillary pressure data based on NMR data, but NMR data are expensive and unstable to obtain. This study aims to accurately predict capillary pressure curves. Based on rock particle size data, various machine learning methods, such as traditional machine learning and artificial neural networks, are used to build prediction models and predict different types of capillary pressure curves, aiming at studying the best prediction algorithm. In addition, through adjusting the amount of particle size characteristic data, the best amount of particle size characteristic data is explored. The results show that three correlation coefficients of the four optimal algorithms can reach more than 0.92, and the best performance is obtained using the Levenberg–Marquardt method. The prediction performance of this algorithm is excellent, with the three correlation coefficients being all higher than 0.96 and the root mean square error being only 5.866. When partial particle size characteristics are selected, the training performance is gradually improved with an increase in the amount of feature data, but it is far less than the performance of using all the features. When the interpolation increases the particle size characteristics, the best performance is achieved when the feature data volume is 50 groups and the root mean square error is the smallest, but the Kendall correlation coefficient decreases. This study provides a new way to obtain capillary pressure data accurately.

The formation and development of fractures increase reservoir heterogeneity and improve reservoir performance. Therefore, it is of great research value to accurately identify the development of fractures. In this paper, two- and three-dimensional models are constructed based on the finite element method and compared with the real axis integration method. The influence of different geometric parameters on the Stoneley wave amplitude is studied to assess the propagation of Stoneley waves in the fracture zone in the well. The results show a significant positive correlation between the width and number of fractures and the attenuation coefficient of Stoneley waves. The fracture angle has a negative correlation with the attenuation coefficient and lesser impact on Stoneley waves. In addition, Stoneley waves are less sensitive to changes in fracture location, while the sensitivity to fracture spacing is significant in the range of 50 cm to 75 cm. The main propagation depth of Stoneley waves occurs 20 cm from the wall of the well. Quantitative analyses of the fracture width, number, location, spacing, depth, and angle are conducted to determine the influence of the fracture parameters on the Stoneley wave attenuation coefficient, clarify Stoneley wave propagation in wells, and provide a theoretical basis for the accurate evaluation of fractures.

In the current context of the global energy landscape, China is facing a growing challenge in oil and gas exploration and development. It is difficult to evaluate the log data because of the lithological composition of igneous rocks, which displays an unparalleled degree of complexity and unpredictability. Against this backdrop, this study deploys advanced multifractal detrended fluctuation analysis (MF-DFA) to comprehensively analyze key parameters within igneous rock logging data, including natural gamma-ray logging, resistivity logging, compensated neutron logging, and acoustic logging. The results unequivocally demonstrate that these logging data possess distinct multifractal characteristics. This multifractality serves as a powerful tool to elucidate the inherent complexity, heterogeneity, and structural and property variations in igneous rocks caused by diverse geological processes and environmental changes during their formation and evolution, which is crucial for understanding the subsurface reservoir behavior. Subsequently, through a series of rearrangement sequences and the replacement sequence on the original logging data, we identify that the probability density function and long-range correlation are the fundamental sources of the observed multifractality. These findings contribute to a deeper theoretical understanding of the data-generating mechanisms within igneous rock formations. Finally, multifractal detrended cross-correlation analysis (MF-DCCA) is employed to explore the cross-correlations among different types of igneous rock logging data. We uncover correlations among different igneous rocks’ logging data. These parameters exhibit different properties. There are negative long-range correlations between natural gamma-ray logging and resistivity logging, natural gamma-ray logging and compensated neutron logging in basalt, and resistivity logging and compensated neutron logging in diabase. The logging data on other igneous rocks have long-range correlations. These correlation results are of great significance as they provide solid data support for the formulation of oil and gas exploration and development plans.

Formation Microresistivity Scanner Imaging (FMI) plays a crucial role in identifying lithology, sedimentary structures, fractures, and reservoir evaluation. However, during the lithology identification process of FMI images relying on transfer learning networks, the limited dataset size of existing models and their relatively primitive architecture substantially compromise the accuracy of well-log interpretation results and practical production efficiency. This study employs the VGG-19 transfer learning model as its core framework to conduct preprocessing, feature extraction, and analysis of FMI well-log images from glutenite formations in an oilfield in Xinjiang, with the objective of achieving rapid and accurate intelligent identification and classification of formation lithology. Simultaneously, this paper emphasizes a systematic comparative analysis of the recognition performance between the VGG-19 model and existing models, such as GoogLeNet and Xception, to screen for the model exhibiting the strongest region-specific applicability. The study finds that lithology can be classified into five types based on physical structures and diagnostic criteria: gray glutenite, brown glutenite, fine sandstone, conglomerate, and mudstone. The research results demonstrate the VGG-19 model exhibits superior accuracy in identifying FMI images compared to the other two models; the VGG-19 model achieves a training accuracy of 99.64%, a loss value of 0.034, and a validation accuracy of 95.6%; the GoogLeNet model achieves a training accuracy of 96.1%, a loss value of 0.05615, and a validation accuracy of 90.38%; and the Xception model achieves a training accuracy of 91.3%, a loss value of 0.0713, and a validation accuracy of 87.15%. These findings are anticipated to provide a significant reference for the in-depth application of VGG-19 transfer learning in FMI well-log interpretation.

Laterolog (DLL) logging, utilizing deep and shallow detection depths, is instrumental in identifying and evaluating complex reservoirs such as coal measures. It effectively captures the resistivity contrast between the virgin zone and the invaded zone, providing crucial data for fluid identification, reservoir quality assessment, and fracture system characterization. However, the DLL response in practice is a complex function of coupled factors including borehole environment and bed thickness. Existing studies have predominantly focused on conventional sandstone or carbonate reservoirs, leaving the response characteristics in low-resistivity reservoirs like coal seams poorly understood, which consequently limits the accuracy of quantitative evaluations in fractured reservoirs. To address this gap, this study established a two-dimensional numerical model for deep and shallow laterolog responses using the COMSOL finite-element platform. We systematically investigated the influence of key parameters—including sonde coefficients, borehole diameter, coal seam thickness, and fracture properties (count, width, and spacing)—on DLL resistivity responses. The results indicate that sonde coefficients are significantly influenced by model dimensions; the shallow laterolog sonde coefficient stabilizes rapidly within the detection range, whereas the deep laterolog sonde coefficient requires a substantially larger model domain to achieve convergence. An increase in borehole diameter causes a simultaneous decrease in both deep and shallow resistivities, characterized by a “positive separation” where the shallow resistivity exhibits a more pronounced drop. A positive correlation exists between coal seam thickness and apparent resistivity; the vertical resolution of the DLL for coal seams is approximately 2.5 m, below which the seam’s response characteristics are readily masked by adjacent formations. Fracture development markedly alters resistivity curve profiles, inducing characteristic depressions. Increasing the number of fractures or their width exacerbates these depressions and lowers the overall resistivity. The shallow laterolog curve often displays a distinct “spike-depression” morphology at fracture zones. Notably, increased fracture spacing facilitates the formation of macroscopic conductive pathways, leading to a further reduction in resistivity. The simulation results demonstrate excellent agreement with field-measured data, validating the high reliability of the model. The findings provide a robust theoretical foundation and methodological reference for refined DLL interpretation, environmental correction, and reservoir quality assessment in coal seams and fractured reservoirs.