Geophysical Prospecting最新文献

The accuracy of fault interpretation is generally influenced by the quality of seismic images. Because of the blurring effect of the migration process, faults with small throws may not be clearly imaged in seismic images, which will impose limitations on the fault detection. To address this issue, we propose a deep learning-based method to enhance faults in poststack seismic images. We generate abundant training samples by convolving the three-dimensional point-spread functions with the noisy reflectivity models. The corresponding labels are synthesized using the one-dimensional seismic wavelet convolution method, simulating conditions with perfect illumination. To train the network for optimal performance, we investigate the impact of different loss functions. Ultimately, we employ a mixed loss function combining structural similarity index measure and gradient difference loss, since the gradient difference loss focuses more on geological edge information, and the structural similarity index measure possesses excellent image perceptual capability and optimization property. Results from one synthetic seismic image and three real seismic data demonstrate that our proposed method can effectively restore the sharpness of fault surfaces, particularly for faults with small displacements. Compared to the structural smoothing method, the network we trained achieves optimal fault enhancement. Furthermore, coherence-based fault images indicate that seismic images enhanced using our method can improve the accuracy of fault interpretation and yield more continuous fault maps.

Marine seismic multiples contain more structure information than primaries and should be considered in migrations. However, multiple migrations suffer from severe crosstalks generated by interferences among undesirable multiples. It has been proven that the water-bottom-related multiple migration can suppress crosstalks greatly. However, if all associated consecutive-order multiples are considered, the computation cost is extremely high. To settle this issue, a phase-encoding-based multiple migration is proposed. Supergathers are first created by randomly phase-encoding consecutive-order multiples and stacking-encoded multiples. By migrating supergathers, the proposed method can fulfil migrations of all order multiples simultaneously, thereby reducing the computation cost significantly. We use a three-layer model and the Pluto 1.5 model for numerical comparisons. The results reveal that the method can retrieve high-quality images and increase computation efficiency considerably.

The heterogeneity in permeability of coal reservoirs is primarily attributed to the considerable variation in the morphologies and structures of microscopic pore-fractures, shaped by complex geological processes. This study emphasizes the necessity of understanding the impact and governance of these morphological and structural variations in pore-fractures across different types of coal bodies on their permeability. Utilizing computerized tomography scanning and three-dimensional imaging, we examined coal samples from the Datong coalfield in the southeastern Qinshui Basin, Shanxi Province, to characterize the pore-fracture morphologies and structures distinct to various coal-body types based on tomographic data. This introduces a methodology for assessing the influence of microscopic pore-fracture parameters, such as porosity, specific surface area, tortuosity and fractal dimension, on permeability sensitivity. This is achieved through the application of the modified Kozeny–Carman equation and a fractal permeability model. Findings indicate a predominance of slab fractures in raw coal, whereas fragmented coal under weak brittle deformation exhibits small, isolated pore-fractures with minimal diameter and volume and poor connectivity. In contrast, granular coal subjected to strong brittle deformation features extensive clusters of large pore-fractures with significant diameter and volume, enhancing connectivity. Moreover, permeability predictions are refined by integrating the modified Kozeny–Carman equation with tomographic data, offering a more precise understanding of the permeability across different coal bodies.

The stratum can be modelled as a horizontal transversely isotropic medium when a single set of vertically parallel fractures embedded in an isotropic background medium, which facilitates efficient study for fractured reservoirs. Elastic parameters and fracture weaknesses are important parameters to describe the characteristics of fractured reservoirs, and seismic inversion plays a significant role in parameters estimation. The commonly used deterministic inversion methods do not fully utilize the prior information and fails to present the uncertainty analysis of inversion results. To address these shortcomings, we propose a Bayesian linearized amplitude variation with offset and azimuth inversion method tailored for horizontal transversely isotropic media, enabling a more robust analysis of uncertainty. Within the framework of Bayesian inversion, the proposed method successfully derives analytical expressions for the posterior mean and covariance of both elastic parameters and fracture weaknesses. The response characteristics of the anisotropic reflection coefficient are analysed, and it is found that the perturbations of elastic parameters have a greater effect on reflection coefficient compared to fracture weaknesses. Synthetic data examples confirm that the accuracy of estimated P- and S-wave velocities and density surpasses that of fracture weaknesses, and the proposed method still performs well for the case of moderate noise. A field data example demonstrates that the inverted profiles agree well with the logging curve, and the estimated fracture weaknesses display significantly high values in the reservoir area. The estimated reservoir parameters not only contribute to a more accurate representation of the fractured gas-bearing reservoir but also provide insights into the target gas reservoir through its posterior distribution. Both synthetic and field data examples demonstrate the stability and reliability of the proposed method in characterizing fractured reservoirs. We determine that the proposed method provides an available tool for nuanced evaluation of uncertainty for the inversion results, and it is helpful for the fine description of fractured hydrocarbon-bearing reservoirs.

The high accuracy and efficiency of traveltime calculation are critical in seismic tomography, migration, static corrections, source locations and anisotropic parameter estimation. The fast-sweeping method is an efficient upwind finite-difference approach for solving the eikonal equation. However, the fast-sweeping method is accurate only along the axis directions. In two-dimensional or higher dimensional cases, the accuracy is severely decreased in the diagonal directions due to the numerical errors in these directions. These similar numerical errors also arose in higher order fast-sweeping method and anisotropic fast-sweeping method. To improve the accuracy of traveltime calculation in two-dimensional or higher dimensional space, a shortest-path-aided fast-sweeping method is proposed. The shortest-path-aided solution is embedded into the sweeping process of the standard fast-sweeping method to improve the traveltime accuracy in the diagonal directions. Shortest-path-aided fast-sweeping method is very easy to implement nearly without additional computational cost and memory consumption. Furthermore, this method is easy to extend from two-dimensional to higher dimensional, from low-order to higher-order and from isotropic to anisotropic cases.

The calculation of the vertical derivatives of potential field methods can be carried out in a stable manner by Tikhonov regularization, but this procedure requires the appropriate selection of a regularization parameter. For this purpose, we introduce a criterion based on Morozov's discrepancy principle that uses a preliminary approximation given by the vertical derivative of the smoothed data. The smoothing may be performed by a physical or a mathematically based low-pass filter. The filtered data are computed only for estimating the regularization parameter; once it is found, we evaluate the regularized vertical derivative from the original data (not from the smoothed one) in the frequency domain. We verified from experiments with noise-corrupted synthetic data, as well as gravity and magnetic field data, that the regularized vertical derivative has about the same smoothness as the one obtained from filtered data, but true anomalies are more easily distinguished from noise and the shapes of the anomalies are better preserved.

Full waveform inversion stands at the forefront of seismic imaging technologies, pivotal in retrieving high-resolution subsurface velocity models. Its application is especially profound when imaging complex geologies such as salt bodies, which are regions notoriously challenging, yet essential given their hydrocarbon potential. However, with the power of full waveform inversion comes the intrinsic challenge of estimating the associated uncertainties. Such uncertainties are crucial in understanding the reliability of subsurface models, particularly in terrains like subsalt regions. Addressing this, we advocate for a nuanced approach employing the Stein variational gradient descent algorithm. Through a judicious use of a limited number of velocity model particles and the integration of random field-based perturbations, our methodology provides a local representation of the uncertainties inherent in full waveform inversion. Our evaluations, based on the Marmousi model, showcase the robustness of the proposed technique. Yet, it is our exploration into salt-intensive terrains, leveraging data from the Sigsbee 2A synthetic model and the Gulf of Mexico, that emphasizes the method's versatility. Findings indicate pronounced uncertainties along salt boundaries and in the deeper subsalt sediments, contrasting the minimal uncertainties in non-salt terrains. However, anomalies like salt canyons present unique challenges, potentially due to the interplay of multi-scattering effects. Emphasizing the scalability and cost-effectiveness of this approach, we highlight its potential for large-scale industrial applications in full waveform inversion, while also underscoring the necessity for prudence when integrating these uncertainty insights into subsequent seismic-driven geological and reservoir modelling.

Since many years ago, ultrasonic velocity has been used to investigate the physical and mechanical behaviour of rocks, thereby playing an important role in reservoir characterization and seismic interpretation. In order to develop the knowledge of ultrasonic tools, I performed a noble analysis on the ultrasonic behaviour of rocks under confining stress and evaluated a distinctive property of porous media that is measured as the area under the stress–velocity curve (here defined as S^*). I further investigated its relationship with elastic and mechanical behaviours of rock. To validate the theoretical framework developed in this work, 20 core plugs from various rock units with complex microstructures were subjected to triaxial compressional tests to calculate their area under the curve. Calculations were made for crack-closing, elastic and post-elastic stages (e.g. pore collapse) along the ultrasonic velocity–stress curve. Moreover, the selected samples had their microstructure investigated by thin-section studies to quantify their porosity and pore type. The results were analysed to check for the effect of pore type on S^* in different stages of the stress–velocity curve. Based on the outputs of the analysis of variance and Pearson's correlation coefficient analysis, the curve had its shape and underlying area closely related to the porosity and pore geometry. Indeed, the results showed that the shale and sandstone with micro cracks and carbonate with stiff pores correspond to smaller and larger areas under the curve in crack-closing and inelastic stages, respectively. Cross-correlating the results to compressibility (inverse of bulk modulus), it was figured out that the calculated area under curve was well consistent with the compressibility. In addition, S^* represents both static and dynamic behaviours of the rock, and the results revealed that the shape and curvature of the stress–velocity curve give valuable information about the rock microstructure. Another finding was the fact that the type of fluid and wave velocity seemingly affect the S^*. Our findings can help interpret wave velocity behaviour in reservoir rocks and other stressful porous media.