Unconventional oil and gas reservoirs have broad exploration and development prospects. Fracture parameters and effectiveness evaluation are two of the key tasks for the evaluation of these types of reservoirs. Array acoustic logging can be used for fracture evaluation to compensate for the deficiencies of the image logging fracture evaluation method. Therefore, to develop acoustic logging evaluation methods as well as nondestructive testing methods for fractures, experiments were conducted to study the shear wave transmission in fractured media. Experiment data demonstrate a good correlation between the shear wave attenuation coefficient and fracture width, and the shear wave attenuation coefficients rise logarithmically with the increase in the fracture width for all models with different porosities and distinct dip angles of fractures. The shear wave attenuation coefficient changes relatively faster with the fracture width when the fracture width is within 250 μm. In addition, the shear wave attenuation is affected by the core porosity and fracture dip angle. When the fracture width is constant, the shear wave attenuation caused by the 0° fracture is relatively larger and is obviously greater than that of the fractures at other angles, which is consistent with the existing experimental results. The results of this study can be used to guide further research on amplitude compensation methods for sonic signal transmission in fractured media and fracture evaluation methods.

Unconventional reservoirs, such as shale and tight formations, have become increasingly vital contributors to oil and gas production. In these reservoirs, fractures serve as crucial spaces for fluid migration and storage, making their precise assessment essential. Array acoustic logging stands out as a pivotal method for evaluating fractures. To investigate the impact of fracture width, fracture-filling conditions, and acoustic frequency on compressional and shear waves, a three-dimensional variable mesh finite difference program was employed for acoustic logging numerical simulation. Firstly, numerical models representing fractured formations with varying fracture widths and distinct fluid-filling conditions were established, and array acoustic logging numerical simulations were conducted at different frequencies. Subsequently, the waveform data were processed to extract acoustic characteristic parameters, such as velocities and amplitude attenuations of compressional and shear waves. Finally, a quantitative analysis was conducted to examine the variation patterns of characteristic parameters of refracted compressional and shear waves in relation to fracture properties. The research results indicate that amplitude attenuation information derived from borehole wave modes is particularly sensitive to the changes in fracture properties. As fracture width increased, we observed a significant amplitude attenuation in both compressional and shear waves, proportional to the logarithm of the attenuation coefficients. Furthermore, when the fracture width was constant, gas-filled fractures exhibited more prominent amplitude attenuation than water-filled fractures, with shear wave attenuation being more sensitive to the filling material. Moreover, from a quantitative perspective, the analysis revealed that the attenuation coefficients of refracted compressional and shear waves exhibited an exponential variation with gas saturation. Notably, once fracture width and filling conditions were established, the amplitudes of compressional and shear waves at the dominant frequency of 40 kHz were significantly reduced compared to those at 8 kHz, accompanied by increased attenuation. Subsequent quantitative analysis revealed that, when the product of fracture width and dominant frequency remains constant, the corresponding attenuation coefficient ratios approach 1. This indicates that the attenuation process of acoustic propagation in fractured media follows the principle of acoustic similarity. The findings of this study provide reference for further research on fracture property evaluation methods based on array acoustic logging data.

Fractures serve as critical pathways for fluid movement and storage within tight reservoirs, playing a pivotal role in the exploration and development of geologic resources. The intricate nature of these fractures, often manifesting as complex belts due to the influence of multifaceted tectonic activities, complicates the understanding of how wave propagation is altered by various fracture characteristics. To address this challenge, we carry out physical experiments and finite-difference numerical simulation studies on scaled physical models of wells based on acoustic logging techniques. This study meticulously examines the interplay between different fracture attributes and the behavior of the S and Stoneley waves, providing a quantitative analysis of the fracture width, density, and ductility. Our findings reveal that the amplitude of the direct Stoneley wave exhibits an exponential decline with an increase in the equivalent fracture width. With a constant equivalent fracture width, an increase in the number of fractures leads to a higher amplitude of the direct Stoneley wave. Moreover, as the radius of the fracture extension expands, the relative amplitude of the Stoneley wave diminishes, following a logarithmic trend.

Precise characterization of fluid‐sensitive logging responses in complex shale reservoirs is very important. It is essential for quantitative formation evaluation and development optimization. Based on a case study of the shale gas reservoir in the second member of the Wujiaping formation (hereinafter referred to as the Wu‐2 Member) in the Hongxing area of the Sichuan Basin, this paper proposes an integrated reservoir evaluation system combining parameter model optimization and machine learning. The study addresses the challenges of calculating reservoir parameters and identifying sweet spots exhibiting shale reservoirs characterized by high carbonate content, high total organic carbon (TOC), high gas content, anomalous resistivity, and thin interbedding. The workflow comprises two main stages. First, four key petrophysical models are enhanced to improve the accuracy of fluid and mechanical property predictions. The specific model is as follows: (1) A TOC calculation model based on a combination of ridge regression and the least squares method; (2) A gas saturation evaluation model based on the neutron‐density overlay method; (3) A calculation model of adsorbed gas corrected for temperature and Langmuir pressure; (4) A brittleness index calculation model based on the dual correction of carbonate mineral content and TOC. Second, precise reservoir classification and evaluation are achieved through a “Random Forest + Clustering” machine learning algorithm. Research indicates that the quantitative criteria for identifying “sweet spots” in the study area are established as follows: TOC >5%, gas saturation >50%, porosity >4.5%, and brittleness index >50%. Based on this standard, the average thickness of sweet spots is 16.03 m (accounting for 81.78% of the total). This framework provides a valuable reference for the exploration and development of analogous gas reservoirs.

The resistivity imaging system is specifically designed for the precise measurement of resistivity distributions within drilled columnar core samples. Its coaxial symmetric configuration enables the non-destructive characterization of electrical properties, with broad applications in oil and gas exploration, reservoir evaluation, and geological research. By integrating a ring return electrode and full-circumference electrode arrays, the system can acquire core-scale resistivity data in conductive media environments. The self-developed imaging software employs advanced processing algorithms—including depth correction, amplitude normalization, and image enhancement—to transform raw resistivity measurements into high-resolution surface imaging maps. Experimental results demonstrate that the system can resolve features such as cracks with a minimum width of 0.5 mm and pores with a minimum inner diameter of 0.4 mm in granite core, providing a novel technical approach for the fine-scale characterization of core materials.