Rock salt (essentially halite) is a special type of sedimentary rock that has played a large role throughout tectonic and economic history. The unique physical properties of halite (ductility, low density, flowability, and impermeability) can be critical factors in hydrocarbon traps and underground storage. However, seismic imaging and interpretation can be challenging when salt structures are present due to their complex geometry and large impedance contrasts relative to surrounding rocks. To investigate the properties of rock salt in terms of elastic parameters, we use ultrasonic laboratory measurements and well logs. In the laboratory, we have analyzed the effects of composition, crystalline structure, pressure, and temperature on the elastic behavior of a variety of rock salt samples. The samples include pure halite (>95 wt%) from the Gulf of Mexico (GOM) area, argillaceous rock salt from the Zipaquirá Mine, Colombia, and crystalline salt from the Goderich Mine, Canada. Current measurements suggest that the GOM salt cores behave isotropically in general. The Zipaquirá salt samples show velocity and density variations on account of their heterogeneous composition. The Goderich halite crystals display distinct cubic anisotropy. Measurements on the GOM samples at varying confining pressures and temperatures indicate that increasing pressure elevates velocity whereas increasing temperature decreases velocity. From the analysis of 145 log suites from boreholes drilled through rock salt in the northern GOM, we found that, within the salt formations, P-wave velocities increased slightly with depth (approximately $5\mathrm{m}/\mathrm{s}$ per km). The S-wave velocities from three wells range from 2280 to $2580\mathrm{m}/\mathrm{s}$. Bulk densities from all the wells cluster at $2160\pm 30\mathrm{kg}/{\mathrm{m}}^3$. These laboratory and log measurements provide new values for the elastic properties of rock salt, which can assist in velocity model building, synthetic seismogram generation, and the understanding of the rock physics of halite.

SUMMARY: Salt structures are widely distributed in many basins worldwide and play an important role in understanding tectonic movements, offering underground storage and sealing hydrocarbon traps. In addition to the acknowledged difficulties in mapping complex salt structures through seismic methods, when an evaporitic layer, such as anhydrite, forms over salt, it can introduce strong multimode conversions that can couple with the primary compressional wavefields and generate artefacts in resulting acoustic images. From two well-log suites from the Gulf of Mexico, we identify thin evaporitic caprocks on top of their salt bodies and analyse their elastic properties. Through controlled experiments, including physical and numerical modelling with a vertical seismic profiling survey geometry, we observe significant shear-mode conversions at the top of the ultra-high-velocity caprock, which further result in a family of prominent S and P (converted from S wave) modes across the top of the salt region. Similarly, in a field survey, we identify evident converted S waves and a multimode P wave (converted S wave in the anhydrite layer, and P wave elsewhere) following the primary P transmission inside the salt body. While separating the converted Smodes at the receiver end is more unambiguous, the multimode P waves could behave very similarly to the primary Pmodes and are more difficult to suppress. Under the common acoustic assumption of seismic velocity model building and imaging, complex mode conversions on top of the salt are generally ignored. Through controlled experiments and a field survey, we analyse the pitfalls associated with this omission. We emphasize the importance of understanding the physics of wave partitioning in the presence of a thin ultra-high-velocity layer on the top of the salt.

Vertical seismic profiling (VSP) helps to derive high-resolution images around the instrumented borehole and is a cost-effective technique for CO ₂ storage monitoring. In routine VSP data processing, P- and S-wave separation is a crucial step to extract independent single-mode waves for accurate imaging and interpretation. Conventional wave mode separation involves tedious, subjective, and non-reproducible manual interventions, especially when dealing with complex geology. To better automate the process, we propose a data-driven deep learning-based P- and S-wave separation method. Our method adapts a fully convolutional neural network that simultaneously extracts P- and S-potential data from multicomponent VSP measurements. To reduce the enormous computational cost in wave simulation while constructing training datasets with sufficient kinematic and dynamic variations, we introduce virtual wellbores where synthetic VSP data sampling wide variations in seismic kinematics and dynamics are recorded using only a dozen elastic wave simulations on a single velocity model. We qualify the separation results both directly in data space and in image space after reverse time migration (RTM). Generalization tests on various synthetic models and their corresponding RTM images demonstrate that the proposed strategy provides sufficient sampling of the high-dimensional data space and essentially ensures successful applications of the trained neural network to similar yet different geological scenarios.

Seismic attributes comprise an effective method for oil and gas reservoir characterization and prediction. Hundreds of seismic attributes have been introduced in the last 30 years. Among the seismic attributes targeting different reservoir features, the autoencoder (AE) receives a significant amount of attention, as it extracts deep attributes of seismic data, providing more details of seismic lateral features than other seismic waveform data and seismic attributes. However, data-driven deep attributes bring new challenges to interpretation as they lack the support of intrinsic physical mechanisms. Hence, a shared AE (S-AE) method is proposed in this article, which can extract seismic deep attributes and fuse traditional seismic attributes simultaneously. An S-AE is a revised version of an AE, which consists of an encoder and decoder. An S-AE takes the seismic waveform as the input of the encoder and obtains the deep attribute, and the decoder then transforms the deep attribute to reconstruct the seismic waveforms and attributes. In an S-AE, the network in front of the decoder is shared, while the networks after the decoder consist of independent layers. Such a network structure ensures the effect of reconstruction and associates seismic attributes with the extracted deep attribute, so as to achieve the purpose of attribute fusion and deep attribute extraction. The proposed S-AE method is compared with conventional seismic data fusion methods, such as RGB and principal component analysis, and the superiority of the S-AE is demonstrated in both synthetic and field applications.

Despite the extensive application of artificial neural networks in seismic inversion, their effectiveness is often hampered by the limited availability of labeled data. To address this challenge, we introduce a novel method for seismic impedance inversion. Our approach integrates a physics-driven cycle network with a conditional generative adversarial network (CGAN) and a convolutional model. Employing seismic data as the input, the CGAN capitalizes on inherent information to minimize non-uniqueness during inversion. Furthermore, the convolutional model, acting as a physics-informed operator, reverts the derived impedance data back to seismic form, enabling simultaneous training of neural networks with labeled and unlabeled data, fulfilling the seismic-to-seismic cycle. The proposed method is demonstrated to be effective on tests using both theoretical models and field data.

We have applied multiscale deformable-layer tomography (DLT) to build a laterally varying velocity model, using a single-offset vertical seismic profile (VSP) data set acquired for a salt proximity survey in southern Texas. The purpose of the VSP survey is to delineate the 2D salt flank using the P-wave reflections. Previous study has identified an anhydrate layer as the cap rock of the salt dome. The large impedance contrasts of this anhydrite layer generate strong downgoing P (sediment)-S (anhydrite)-P (salt) waves recorded by downhole geophones. Incidentally, the P-S-P-waves have traveltimes similar to those of the P-wave salt flank reflections, thus contaminating the imaging of the salt flank. Identifying shear-mode contamination requires an accurate velocity model of anhydrite. However, the extremely poor coverage of the single-offset VSP greatly challenges tomographic techniques to determine the lateral velocity variation. We tackle this problem using multiscale DLT, which characterizes the velocity field by a set of deformable layers. We constrain the layer velocities using the check-shot data and invert for the geometric variation. The inverted model indicates that the anhydrite layer has a “thick-thin-thick” lateral variation with offset, and the S-wave in the anhydrite layer helps in imaging the P-S-P-waves along the well track. The estimated anhydrite layer geometry is validated by the kinematic accuracies of P-waves in the data domain and P-S-P-waves in the image domain. Some in-salt dipping structures are determined by multiscale DLT as well. This field data example indicates that multiscale DLT is feasible for estimating velocities using VSP data of the single-offset situation. An accurate velocity model is the key for modeling and adaptive subtraction of the shear-mode contamination related to the salt geometry.