Reflection waveform inversion (RWI) exploits reflected-wave information in seismic data to update the deep background velocity model. By alternately inverting for the migration and tomographic components, RWI not only improves the accuracy of deep velocity model updates but also alleviates the cycle-skipping problem to a certain degree. However, RWI generally requires seismic data with a high signal-to-noise ratio (SNR) and has so far achieved its most successful applications in marine environments. In contrast, land seismic data are often degraded by poor receiver coupling, rugged topography, environmental noise, and strong surface-wave interference, making it difficult to acquire continuous and high-SNR reflection waveforms, which severely limits the applicability of RWI to land data. To address these challenges, this study employs Kirchhoff pre-stack time migration to identify characteristic reflection layer and extract their corresponding common-image gathers (CIGs). The extracted events are then reverse migrated to reconstruct reflected-wave data with enhanced SNR. The reconstructed data are subsequently incorporated into RWI and validated using both synthetic and field data examples. The results demonstrate that the proposed method significantly improves the accuracy of deep background velocity model updates. Furthermore, the strong consistency between the migrated images and the corresponding CIGs confirms the reliability and effectiveness of the reconstructed reflection data for RWI applications. Overall, this method offers a new feasible solution for applying RWI to land seismic data.

Formation drillability assessment is crucial for drilling operations, as it directly influences operational efficiency and cost-effectiveness. Traditional three-dimensional (3D) assessment methods often face challenges due to the unstable integration of multi-source and cross-scale data, resulting in limited spatial generalization and suboptimal prediction performance. To address these limitations, this paper proposes a multi-source data fusion method based on a gated recurrent unit (GRU) network to enhance intelligent formation drillability assessment and improve drilling efficiency in a study area in eastern China. The method consists of two phases: well data training and 3D application. In the first phase, pseudo-depth domain seismic records synthesized from seismic average wavelets and well logging data serve as the foundation. Sensitive attributes related to formation drillability are further extracted as network inputs. These sensitive attributes include a velocity model incorporating geological information and a seismic frequency-fraction attribute that captures multi-scale stratigraphic structure. A corrected drillability index (D_c) is used as a label for model training, ensuring that the network learns to establish an accurate mapping relationship between input attributes and drillability indicators. This training method leverages the temporal and sequential learning capabilities of the GRU network to effectively model complex relationships in the data. In the second phase, the pretrained network was extended to 3D applications, constructing a 3D input dataset by extracting the corresponding attributes. This dataset was then fed into a pretrained GRU model to predict formation drillability in the study area. Analysis of five representative wells in the study area validated the effectiveness of D_c in characterizing rock drillability in the study area. Furthermore, experiments using the Marmousi numerical model demonstrated that the method outperformed traditional intelligent prediction methods, such as those relying solely on raw seismic data or a combination of raw seismic and well logging data. Practical application in the study area further confirmed the method’s ability to effectively capture variations in formation drillability. By providing reliable predictions, the method becomes a powerful tool for optimizing drilling operations and enhancing drilling engineering decision-making.

Knowledge about the seismic elastic modulus dispersion, and associated attenuation, in fluid-saturated rocks is essential for better interpretation of seismic observations taken as part of hydrocarbon identification and time-lapse seismic surveillance of both conventional and unconventional reservoir and overburden performances. A Seismic Elastic Moduli Module has been developed, based on the forced-oscillations method, to experimentally investigate the frequency dependence of Young's modulus and Poisson's ratio, as well as the inferred attenuation, of cylindrical samples under different confining pressure conditions. Calibration with three standard samples showed that the measured elastic moduli were consistent with the published data, indicating that the new apparatus can operate reliably over a wide frequency range of f∈[1–2000, 10⁶] Hz. The Young's modulus and Poisson's ratio of the shale and the tight sandstone samples were measured under axial stress oscillations to assess the frequency- and pressure-dependent effects. Under dry condition, both samples appear to be nearly frequency independent, with weak pressure dependence for the shale and significant pressure dependence for the sandstone. In particular, it was found that the tight sandstone with complex pore microstructure exhibited apparent dispersion and attenuation under brine or glycerin saturation conditions, the levels of which were strongly influenced by the increased effective pressure. In addition, the measured Young's moduli results were compared with the theoretical predictions from a scaled poroelastic model with a reasonably good agreement, revealing that the combined fluid flow mechanisms at both mesoscopic and microscopic scales possibly responsible for the measured dispersion.

Prediction of seismic attenuation and dispersion that are inherently sensitive to hydraulic and elastic properties of the medium of interest in the presence of mesoscopic fractures and pores, is of great interest in the characterization of fractured formations. This has been very difficult, however, considering that stress interactions between fractures and pores, related to their spatial distributions, tend to play a crucial role on affecting overall dynamic elastic properties that are largely unexplored. We thus choose to quantitatively investigate frequency-dependent P-wave characteristics in fractured porous rocks at the scale of a representative sample using a numerical scale-up procedure via performing finite element modelling. Based on 2-D numerical quasi-static experiments, effects of fracture and fluid properties on energy dissipation in response to wave-induced fluid flow at the mesoscopic scale are quantified via solving Biot's equations of consolidation. We show that numerical results are sensitive to some key characteristics of probed synthetic rocks containing unconnected and connected fractures, demon-strating that connectivity, aperture and inclination of fractures as well as fracture infills exhibit strong impacts on the two manifestations of WIFF mechanisms in the connected scenario, and on resulting total wave attenuation and phase velocity. This, in turn, illustrates the importance of these two WIFF mechanisms in fractured rocks and thus, a deeper understanding of them may eventually allow for a better characterization of fracture systems using seismic methods. Moreover, this presented work combines rock physics predictions with seismic numerical simulations in frequency domain to illustrate the sensitivity of seismic signatures on the monitoring of an idealized geologic CO₂ sequestration in fractured reservoirs. The simulation demonstrates that these two WIFF mechanisms can strongly modify seismic records and hence, indicating that incorporating the two energy dissipation mechanisms in the geophysical interpretation can potentially improving the monitoring and surveying of fluid variations in fractured formations.