Zhihui Zou

The interpretability of seismic images of Earth’s interior properties relies on the quantifiable level of image fidelity. We recommend evaluating seismic image fidelity via three quantifiable elements: image resolution, artifact level, and position accuracy. Though the resolution of seismic images is routinely assessed, it is difficult to detect deceiving image artifacts or evaluate the accuracy of image position beyond the drilling limit. Many image artifacts, such as fake or distorted features, are generated by mistaking the signals in the imaging process or due to limitations in seismic illumination and imaging methods. Most image position errors are produced by erroneous velocity models used in seismic imaging. To ensure seismic image fidelity, we should establish practical evaluation standards during the processes of making and interpreting seismic images. For mitigating image artifacts and position errors, we should analyze their causes and follow practical rules, such as “from known to unknown”, in evaluating and interpreting seismic images.

Accurate characterization of elastic properties near boreholes is critical for reservoir evaluation, well integrity assessment, and high-resolution formation imaging. Traditional borehole inversion methods often suffer from limited resolution and complex implementation, particularly in scenarios involving radial heterogeneity and thin layers. In this study, we present a novel borehole full waveform inversion framework based on automatic differentiation and recurrent neural networks. By reformulating the elastic wave equation in cylindrical coordinates as a recurrent structure, forward modeling becomes naturally embedded in a deep learning framework. Gradient computation is performed using automatic differentiation, which eliminates the need for manually derived adjoint equations and enables flexible implementation of multi-parameter inversion. We validate the proposed method through numerical experiments on a series of synthetic models with increasing geological complexity, including radially layered formations, curved interfaces, and thin low-velocity layers. We further investigate the effects of different acquisition configurations, such as source-receiver offsets, sparse sampling, and unidirectional observation setups. Results show that the proposed method can recover both P-wave and S-wave velocities with high accuracy, even resolving sub-wavelength thin beds beyond the capability of conventional velocity estimation method using logging data. The approach also demonstrates robustness under reduced acquisition coverage and practical logging conditions. Our findings suggest that the proposed framework offers a powerful and flexible solution for high-fidelity borehole imaging, with broad applicability in geophysical exploration and logging interpretation.

Slow formations, characterized by shear-wave velocities lower than those of the borehole fluid, present significant challenges for shear-wave velocity estimation using monopole acoustic logging, primarily due to the absence of critically refracted shear waves. To address this limitation, a borehole full-waveform inversion framework is proposed in this paper, which employs low-frequency monopole excitation to exploit the sensitivity of Stoneley waves to shear velocity. The elastic wave equation is reformulated in cylindrical coordinates as a recurrent neural network structure within a deep learning framework, allowing automatic differentiation for efficient gradient computation without adjoint-state methods. Numerical experiments reveal that while high-frequency monopole data can accurately recover compressional-wave velocities, they fail to resolve shear-wave velocities due to weak Stoneley energy in the high-frequency data. In contrast, strong low-frequency Stoneley waves enable robust and reliable shear-wave inversion. An inversion workflow is further proposed, in which an initial shear-wave velocity model is derived by applying a velocity ratio to the inverted compressional-wave model and subsequently refined through inversion of low-frequency monopole data. The proposed approach yields high-accuracy shear velocity profiles in the near-wellbore region and remains effective under complex geological conditions, including small-scale anomalies and ultra-slow formations. These results highlight the critical role of Stoneley waves in monopole-based inversion and offer a practical solution for estimating the shear-wave velocities of slow and unconsolidated formations. Document Type: Original article Cited as: Xu, S., Liu, Y., Zou, Z., Liu, Y. Borehole full-waveform inversion of monopole logging in slow formations: Insights for shear-wave velocity profiling. Advances in Geo-Energy Research, 2025, 17(1): 82-90. https://doi.org/10.46690/ager.2025.07.07

Perforation is essential for the development of unconventional oil and gas, hydrogen, geothermal energy, and deep subsurface exploration, yet an effective evaluation method has remained elusive. The multiscale tomography (MST) method is applied to analyze downhole perforation, improving inversion accuracy through the multiscale discretization of velocity model grids. Numerical experiments demonstrate the method’s reliability in determining perforation depth intervals, radial penetration depths, and velocity variations outside the well. Simulations before and after perforation reveal significant traveltime changes, supported by logging data showing delays and velocity shifts. A comparison of imaging results with various receiver configurations highlights the critical role of receiver arrangement in achieving accurate subsurface imaging. These findings validate MST as an effective tool for perforation assessment and velocity imaging around wells, with an emphasis on optimal receiver configuration.

Understanding the properties of rocks surrounding boreholes is crucial for industries such as oil and gas exploration. This paper presents an offset-controlled localized velocity inversion method in the τ − p domain, aimed at enhancing the accuracy and computational efficiency of velocity imaging around boreholes. By incorporating positional information from receiver arrays into velocity inversion process, the method more accurately reflects depth and radial variations in acoustic velocity. Numerical models validate the applicability of the method, showing that receiver arrays with a near-to-far configuration should be selected for logging observation systems to effectively capture radial velocity variations. Additionally, considering the offset improves the accuracy of estimating formation velocity variations, particularly for sudden changes such as thin layers. The effectiveness of this approach is further verified using actual borehole logging data, demonstrating its ability to capture velocity variations around wells and providing a new means for interpreting logging data.

SUMMARY: The Three Gorges (TG) in central China is an earthquake prone region that lacks active-source seismic surveys, hence requiring high-resolution passive seismic imaging to reveal crustal structures in detail. While teleseismic virtual-source reflection (TVR) profiling is effective for imaging gently dipping upper crustal structures, it is unsuitable for mapping steep structures beneath rugged topography in the TG region. Here we develop a teleseismic virtual-source reverse time migration (TV-RTM) to improve the imaging of steep structures. Synthetic tests are conducted to demonstrate the validity and resolution of the TV-RTM method. To mitigate the imaging inaccuracy due to sparse station spacing, we interpolate direct-arrival waveforms to achieve a doubling of the minimally required station spacing from 2 to 4 km. The TV-RTM images of the upper crustal structure beneath a 2-D array in the TG region reveal significant fold and thrust structures that correlate well with the surface geology and tectonic framework. The resolution of the images is assessed using 2-D and 3-D synthetic models with the station and source geometry of field data. The TV-RTM method provides a new passive seismic solution for studying upper crustal structures in regions that lack active-source seismic data.

In complex near-surface geologic settings, first-arrival traveltime tomography suffers from strong data noise and uneven raypath coverage. To reduce the inversion non-uniqueness, model smoothing is necessary but often without incorporating structural constraints based on seismic images. The reasons include lack of shallow reflections, strong noise, and gradient velocity field with strong lateral variation. We propose to estimate the near-surface structural trend from the velocity solution of deformable-layer tomography (DLT), that inverts for velocity contours directly. Then the structural dips derived from the DLT solution are taken as the structural regularization in the subsequent traveltime tomographic inversion. Tests for the 2011 SAGEEP workshop model illustrate that this DLT-guided structural regularization steers the tomographic inversion toward more accurate near-surface models. The application of the DLT-guided structural regularization to a field data reveals the near-surface geology of a beach area in Qingdao, China. The DLT solution could complement seismic images or other structural information in regularizing tomographic inversion.

Monitoring underground fluid migration caused by injection/production processes is crucial for guiding petroleum exploitation in mature oilfields and ultimately enhancing petroleum production. In this paper, we propose a time-lapse reverse time imaging (RTI) to dynamically monitor the injection/production processes within oilfield. By using RTI to track microseimicities at different time periods, we can analyze the relationship between injection/production activities and the spatiotemporal changes in microseismic distribution. The inferred relationship enables the time-lapse RTI to infer fluid migration patterns within oil reservoirs. To assess the accuracy and spatiotemporal resolution of the time-lapse RTI, we conducted numerical experiments to evaluate the imaging quality under different microseismic distribution scenarios. In addition, we assessed the method's stability under low signal-to-noise ratio conditions. Numerical results indicate that the time-lapse RTI can effectively distinguish the spatiotemporal variations of seismic swarms at depths of 0.5 kilometers within the target layer, even in the presence of strong noise. Practical applications show a significant correlation between changes in swarm distribution surrounding reservoirs and fluctuations in oil production. Using time-lapse RTI enables real-time monitoring of oilfield injection/production processes, thereby offering valuable insights for optimizing oilfield development and fostering future increases in petroleum production.