Shear wave splitting (SWS) analysis has been widely employed for fracture characterization in both global seismology and seismic exploration. Two key SWS attributes—fast shear wave polarization and the time delay between fast and slow shear waves—can be inverted from four-component seismic data (two horizontal sources and two horizontal receivers). These SWS attributes enable the characterization of subsurface fracture parameters, such as fracture strike and density. In this study, a nine-component vertical seismic profile (VSP) survey was acquired in the Sanhu Depression of the eastern Qaidam Basin, northwestern China. Preliminary analysis of the shear-wave source VSP data reveals two distinct SWS signatures at different depths, corresponding to two separate fractured layers. However, characterizing multiple fractured layers presents significant challenges, as the SWS attributes of deeper fractured layers are strongly influenced by those of overlying fractured formations. Existing approaches for predicting multi-layer fracture parameters are predominantly data-driven and are largely limited to qualitative analysis. To address these challenges, we propose a robust, rock-physics-model-guided method that enables the quantitative estimation of both the fracture strike and fracture density in multiple fractured layers. First, the parameters of the shallow fractured layer are directly estimated from the SWS attributes. Then, synthetic VSP records of the deeper fractured layer are modeled by incorporating Hudson's theory and the reflectivity method. The fracture parameters of the deep fractured layer are inverted by minimizing the difference between the SWS attributes of synthetic records and those of the actual seismic data. A hierarchical search strategy (coarse-scale + refined-scale) is employed to accelerate convergence toward the optimal solution. This investigation provides a practical tool for quantitative characterization of subsurface formations with multiple fractured layers.

Seismic AVO/AVA (amplitude-versus-offset or amplitude-versus-angle) analysis, based on prestack seismic angle gathers and the Zoeppritz equation, has been widely used in seismic exploration. However, conducting the multi-parameter AVO/AVA inversion using only PP-wave angle gathers is often highly ill-posed, leading to instability and inaccuracy in the inverted elastic parameters (e.g., P- and S-wave velocities and bulk density). Seismic AVO/AVA analysis simultaneously using both PP-wave (pressure wave down, pressure wave up) and PS-wave (pressure wave down, converted shear wave up) angle gathers has proven to be an effective method for reducing reservoir interpretation ambiguity associated with using the single wave mode of PP-waves. To avoid the complex PS-wave processing, and the risks associated with PP and PS waveform alignment, we developed a method that predicts PS-wave angle gathers from PP-wave angle gathers using a deep learning algorithm—specifically, the cGAN deep learning algorithm. Our deep learning model is trained with synthetic data, demonstrating a strong fit between the predicted PS-waves and real PS-waves in a test datasets. Subsequently, the trained deep learning model is applied to actual field PP-waves, maintaining robust performance. In the field data test, the predicted PS-wave angle gather at the well location closely aligns with the synthetic PS-wave angle gather generated using reference well logs. Finally, the P- and S-wave velocities estimated from the joint PP and PS AVA inversion, based on field PP-waves and the predicted PS-waves, display a superior model fit compared to those obtained solely from the PP-wave AVA inversion using field PP-waves. Our contribution lies in firstly carrying out the joint PP and PS inversion using predicted PS waves rather than the field PS waves, which break the limit of acquiring PS-wave angle gathers.

Existing studies indicate that gas hydrate-bearing formations exhibit notable seismic velocity dispersion and attenuation. The Shenhu area of the South China Sea hold significant gas hydrate resource potential; however, the relationship between seismic velocity dispersion, attenuation properties, and gas-hydrate saturation remains insufficiently understood. Furthermore, a significant mismatch exists between the real seismic angle gather near a well and the synthetic angle gather generated using the convolution method, and this discrepancy may arise from the seismic velocity dispersion and attenuation characteristics of the gas hydrate-bearing formations. In this paper, we develop a rock physics model that integrates White's and Dvorkin's models, accounting for varied types of gas-hydrate occurrence states, specifically tailored to the gas hydrate-bearing formations in the Shenhu area. This model is calibrated with well log data and employed to investigate how gas-hydrate saturation influences seismic velocity dispersion and attenuation. Numerical analysis reveals the coexistence of two types of gas-hydrate occurrence states in the region: high gas-hydrate saturation formations are dominated by load-bearing-type gas hydrate, and formations containing both gas hydrate and free gas may exhibit either load-bearing or pore-filling types. The seismic velocity dispersion and attenuation properties vary significantly depending on the gas-hydrate occurrence state. We further apply the proposed model to generate seismic velocity and attenuation logs at various frequencies. These logs are used in seismic forward modeling employing both the convolution method and the propagator matrix method. Well tie analysis indicates that the synthetic angle gather incorporating attenuation via the propagator matrix method aligns more closely with the real seismic angle gather than the convolution method. This study provides valuable insights into frequency-dependent amplitude versus offset (AVO) analysis and the seismic interpretation of gas hydrate-bearing formations in the South China Sea.

Oil and gas are important energy resources and industry materials. They are stored in pores and fractures of subsurface rocks over thousands of meters in depth, making the finding and distinguishing them to be a significant challenge. The geophysical methods, especially the seismic and well-logging methods, are the effective ways to identify the oil and gas reservoirs and are widely used in industry. Due to the complexity of near surface and subsurface structures of new exploration targets, the geophysical methods based on advanced computation methods and physical principles are continuously proposed to cope with the emerging challenges. Thus, some new advances in theory and technology of oil and gas geophysics are summarized in this editorial material, especially focusing on the geophysical data processing, numerical simulation technology, rock physics modeling, and reservoir characterization.

The multiscale rock physics of unconventional reservoirs have drawn increasing attention in recent years, which involves several essential issues, including measuring method, transport property, physics model, characteristic scale, and their application. These issues vastly affect science and engineering regarding the exploration and development of unconventional reservoirs. To encourage communication on the advances of research on the rock physics of unconventional reservoirs, a conference on Multiscale Rock Physics for Unconventional Reservoirs was jointly organized by the journals Energies and Advances in Geo-Energy Research. Due to the limitations of movement caused by COVID-19, 21 experts introduced their work online, and the conference featured the latest multiscale theories, experimental methods and numerical simulations on unconventional reservoirs.