Abstract Because of their relatively shallow volume of investigation, borehole acoustic measurements can be affected by abnormal near‐wellbore conditions such as irregular calliper, drilling‐induced formation damage and mud‐filtrate invasion, among others. Additionally, borehole‐slowness measurements inherently deliver rock elastic properties spatially averaged across the length of the multi‐receiver array included in the waveform acquisition system. The consequence is that the interpretation of borehole acoustic measurements needs to account for both radial variations of elastic properties and axial spatial averaging effects across thinly laminated formations before conducting seismic‐well log ties and rock physics interpretations. We introduce an inversion‐based interpretation method to estimate radial shear‐slowness variations from frequency‐dependent slownesses in vertical wells penetrating horizontally layered formations. The inversion procedure is efficiently implemented with an optimized two‐dimensional fast‐forward‐modelling method that simulates borehole acoustic modes in the presence of invaded and thinly laminated formations. Furthermore, the inversion method consists of two sequential steps: Firstly, layer‐by‐layer dispersion slownesses are processed to mitigate axial spatial averaging effects on borehole acoustic measurements; secondly, radial variations of elastic properties are estimated from inversion results obtained from the first step. The new inversion‐based interpretation workflow estimates a layer‐by‐layer single‐radial‐step shear slowness model, including altered and virgin radial zones. By considering both radial and axial averaging effects, the implementation of the two‐step inversion‐based interpretation method in thinly laminated and radially invaded formations consistently improves the definition of shear slowness between invaded and virgin radial zones compared to traditional radial‐profiling methods. The accuracy of the estimated shear slowness heavily relies on the sensitivity of dispersion modes to radial variations in formation shear slowness.

Abstract Coal seams exhibiting nearly horizontal bedding, and fractures can be characterized as transversely isotropic media with a vertical axis of symmetry, known as vertical transverse isotropy coal seams. The resulting anisotropy cannot be overlooked in high‐precision seismic velocity analysis, migration imaging and pre‐stack inversion. Therefore, we estimate the anisotropy of the vertical transverse isotropy coal seam (using the anisotropic Thomsen's parameters ε, γ and δ) by inverting the horizontal P‐ and SH‐wave velocities and fracture density based on a rock physics model. The Mori–Tanaka model and Brown–Korringa formula were first used to quantify the anisotropy of the vertical transverse isotropy coal seams impacted by dry and fluid‐saturated fractures. Subsequently, we formulated an equation for the inversion of horizontal P‐ and SH‐wave velocities, considering the measured vertical P‐ and SH‐wave velocities as constrained parameters. This approach is generally applied in vertical drilling scenarios. We tested the inversion method using the ultrasonic test results of coal samples collected from the southern margin of the Qinshui Basin, China and then used it on the full waveform logging data from a vertical coalbed methane well. Both the inversion results of horizontal P‐ and SH‐wave velocities and the estimated anisotropic parameters (ε and γ) were in good agreement with the ultrasonic test results of coal samples, although the accuracy of δ was slightly lower. Therefore, we believe that the proposed method can be extended to estimate the anisotropy of vertical transverse isotropy medium (assuming suitable rock physics models) for the ultrasonic testing of rock samples and full waveform logging along the vertical direction in near‐horizontal formations.

Abstract The seismic attributes of water‐rich sandstone contain much information about the rock's physical properties and seismic wave parameters. They are commonly used to predict the rock's physical properties (e.g. porosity). However, the seismic attributes of water‐rich sandstone are affected by porosity, water saturation and thickness. To eliminate the influence of thickness on the porosity prediction of water‐rich sandstone and improve the accuracy of the porosity prediction, the authors propose a Lambert W–R transform method to isolate the contribution of thickness and porosity from seismic attributes. First, a rock physical model is used to calculate the equivalent elastic parameters of water‐rich sandstones with different porosity values and water saturation levels. Second, the seismic attribute dataset of water‐rich sandstone is established by forward modelling the seismic response of the wedge‐shaped water‐rich sandstone model, and the selection of sensitive physical properties is completed. Then, the transformation parameters (ζAhRs and ηAhRs) are obtained by Lambert W–R transformation, which is exponentially related to instantaneous amplitude. ζAhRs and ηAhRs are sensitive to thickness and porosity, respectively. Finally, an interpretative template for porosity prediction of water‐rich sandstone is established by cross‐plot analysis (ζAhRs and ηAhRs) and verified by a practical case. The verification results show that the porosity predicted by the interpretation template is consistent with drilling fluid consumption. However, it is lower than the porosity of logging constrained P‐wave impedance inversion.

Abstract The critical angle plays a crucial role in data processing for refraction seismology. In the context of three‐dimensional data, the critical angle exhibits azimuthal dependence, particularly in the presence of an anisotropic model. In this paper, we propose a method to determine the critical angle (phase angle) and analyse the sensitivity of the critical angle to the model parameters and the available azimuthal range for both transversely isotropic medium with a vertical symmetry axis and orthorhombic models. For more complex orthorhombic models, the critical angle can be computed while considering changes in azimuth. In a numerical example, we apply sensitivity analysis to examine the existence of the critical angle and determine its corresponding values to variations in model parameters and the azimuthal range. Additionally, we conduct computations of the critical angle for the simplified acoustic and elliptical orthorhombic models. This analysis can be extended to encompass all model parameters for different wave modes (pure and converted waves), and they provide generalized predictions for the available range of data in seismic data processing applications.

Abstract Recently, we established a blended‐acquisition method: temporally signatured and/or modulated and spatially dispersed source array that jointly uses various signaturing and/or modulation in the time dimension and dispersed source array in the space dimension. We acquired and processed the first pilot programme with our method onshore Abu Dhabi. In this paper, we review the resulting acquisition‐productivity enhancement in the time dimension and discuss it in the space dimension as well. We then review the deblended‐data‐reconstruction processing, followed by imaging processing, and discuss their performance and resulting data quality. We last establish a relationship between the acquisition productivity and the processing performance. We found that this method significantly enhances the acquisition productivity compared to conventional blending methods. For the processing performance, the deblended data can successfully be reconstructed from the blended data; afterwards, the subsurface sections can naturally be imaged from the deblended data. Furthermore, this method owns a relationship: The deblended‐data‐reconstruction performance increases with the acquisition time (i.e. the acquisition effort that is inversely proportional to the acquisition productivity) up to a plateau; the imaging processing improves the data quality as the flow makes progress and eventually reaches a high data quality after poststack processing, regardless of the acquisition effort.

Abstract Many different magnetotelluric response functions such as the impedance and tipper which are converted from measured electromagnetic fields to remove the effects of natural sources have been proposed and studied. To obtain good inversion results, it is important to know that the inversion results depend on the sensitivity patterns of the magnetotelluric response functions used as the input, indicating parts to which the magnetotelluric responses are sensitive. We introduce how to use the sensitivity patterns to determine optimal input magnetotelluric response functions for interpreting field data. For magnetotelluric field data, we use the data acquired at the Utah Frontier Observatory for Research in Geothermal Energy survey area. The target structure in the Utah Frontier Observatory for Research in Geothermal Energy survey area is associated with geothermal energy, and most of the sites are located to the left of the target structure. Examining the sensitivity patterns of the major magnetotelluric response functions, that is the components of the impedance tensor and tipper vector, for the target anomaly, the pattern of y‐component of the tipper vector () covers the areas where most of the sites are distributed. This means information about the target structure is mainly contained in . To investigate the impact of on inversion of the target anomalous body, we compare the inversion results for four cases that (1) the xy‐ and yx‐components of the impedance tensor ( and ) are used; (2) , and are used; (3) , and x‐component of the tipper vector () are applied; and (4) the all components of the impedance tensor (, , and ) are used as the input. This comparison shows that selected as the optimal input from the sensitivity patterns for this case study contributes to retrieving the target anomaly. Through the case study for the Utah Frontier Observatory for Research in Geothermal Energy site, we show how to design a strategy of selecting optimal magnetotelluric response functions based on the sensitivity patterns to effectively image target structures. Our experiment supports that the sensitivity patterns can be used to increase the reliability of magnetotelluric inversion.

Abstract Processing land seismic data, especially vibroseis data, is often challenging due to complicated near‐surface situations and source‐generated noise. The case study deals with noisy vibroseis data acquired in the Jaisalmer Basin. The near‐surface estimation in this area is difficult due to a possible velocity reversal manifested in the shingled patterns of the first breaks. The near‐surface workflow incorporates a model‐adaptive first break‐picking approach, essentially integrating two problems of first‐break‐picking and model estimation into a single problem. The signal‐conditioning workflow is based on cascaded scaling and single‐channel‐based noise reduction to prevent the removal of weak signals. Horizon‐based migration velocity analysis was used to focus reflectors on the constant velocity‐migrated stacks. This was particularly useful in areas with dubious velocity trends based on semblance panels. The velocity volume has structural consistency, which provides a better time‐migrated image. The workflow also incorporates a targeted post‐stack processing sequence to enhance continuity, sharpen discontinuities and improve the resolution, as notable by comparing the legacy‐processing results of the same dataset.

Abstract Most seismic data used for conventional exploration lack far‐offset signals and effective low‐frequency components. This makes it difficult for conventional full waveform inversion methods to recover the low‐wavenumber components of mid‐ and deep‐layer models. To resolve the bottleneck of ray‐theoretical reflection traveltime tomography, wave equations, such as reflection inversion methods, are receiving increasing attention. We reconstructed a wide‐spectrum velocity model based on the multiscale wave‐equation reflection inversion method for model decomposition. First, using the dynamic image warping method to obtain reflection traveltime residuals, the wave‐equation reflection traveltime inversion was used to recover the low‐wavenumber components of the background model, which also solved the cycle‐skipping problem. Second, the medium‐wavenumber component of the model is then supplemented by the reflection waveform inversion, while the reflectivity model is obtained relying on the least‐squares reverse time migration method. Based on this method, the background and perturbation models are updated alternately and iteratively. At the same time, the stratum structural tensor information was extracted using the perturbed model image to construct a preconditioned operator for the stratum structural constraint, suppress unreasonably high‐wavenumber components in the generalized gradient and improve the geological consistency of the inversion results. Testing of the model using the Sigsbee2b model and seismic field data from the East China Sea showed that, compared with the conventional reflection traveltime tomography method based on prestack depth migration, the wave‐equation reflection inversion strategy with well‐matched information from traveltimes to waveforms significantly improved the accuracy of the middle and deep velocity modelling and enhanced the imaging quality of the whole body.

Abstract Shales are rocks with a complex structure. Shales contain high clay content, which constitutes a load‐bearing skeleton. In this study, we present a novel rock physics model to obtain elastic stiffness coefficients of both clays and shales. The robustness of the model is then verified by a field dataset from Eagle Ford shale. We utilize the extended Maxwell homogenization scheme as a rock physics model for transversely isotropic media, which honours the aspect ratio of each inhomogeneity embedded in an effective inclusion domain. Estimated anisotropy parameters ε, γ and δ, on average, are 0.19, 0.29 and 0.04, respectively, based on our modelling results in Eagle Ford shale. Anisotropic modelling results exhibit a good correlation with dipole sonic logs. Both dipole sonic log analysis and rock physics results demonstrate that clay content is the main driver of anisotropy in the field, and there is a direct relationship between clay volume and anisotropy parameters of ε and γ. The method shown here can be readily applied to other unconventional reservoirs.