Geophysical Prospecting - Volume 72, Issue 5, 2024

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.

Wave‐induced fluid flow is considered to be a major source of seismic attenuation and dispersion in porous rocks. From the physical description of partially saturated reservoirs, numerous analytical solutions based on upscaling homogenization theories have been employed to calculate equivalent frequency‐dependent poroelastic media. Nevertheless, dispersion and attenuation predictions are often not reasonably consistent with laboratory and field measurements in a broad frequency range, particularly due to influences of biphasic fluids and their distribution, presence of heterogeneities on various length scales, and pore microstructure. We investigate the role of pore microstructure on pressure and fluid saturation dependence of elastic velocities in tight sandstones. Previous work points out that differentiating the impacts of heterogeneities at various scales on dispersion within seismic exploration and sonic frequencies can be very difficult. In practice, this is because fluid‐related dispersion mechanisms are impossible to be independent. Thus, it is important for a theoretical and more quantitative analysis of the relative contribution of interrelated energy dissipation processes through a better understanding of combined influences due to the presence of microscopic and mesoscopic heterogeneities. Based on microscopic squirt flow and mesoscopic flow in a partially saturated medium, we develop a poroelastic model that allows evaluating the overall frequency‐dependent dispersion via considering a random distribution of fluid heterogeneities as well as the broadly distributed aspect ratio of compliant pores. Experimental validation of the model is accomplished via a comprehensive comparison of predictions with measurements of partially saturated velocities versus pressure and fluid for sandstones with specific pore microstructures.

As part of the Synergetic Utilisation of CO storage Coupled with geothermal EnErgy Deployment project, investigating CO reinjection with different seismic methods, both passive and active seismic surveys have been conducted at the geothermal power plant at Hellisheiði, Iceland. During the 2021 survey, two geophone lines recorded noise for a week. We process the passive‐source data with seismic interferometry to image the subsurface structure around the CarbFix2 reinjection reservoir. To improve image quality, we perform an illumination analysis to select only noise panels dominated by body‐wave energy. The results show that most noise panels are dominated by air‐wave energy arriving from the direction of the power plant. We use panels with a near‐vertical incidence to create a zero‐offset image and a larger selection of body‐wave‐dominated panels to create virtual common‐shot gathers. We process the gathers with a simple reflection seismology processing workflow to obtain stacked images. The zero‐offset images show a relatively lower signal‐to‐noise ratio and only horizontal reflectors. The stacked images show slightly dipping reflectors and possibly lateral amplitude variations around the expected injection region. This could indicate a region of interest for future research into the reinjection reservoir.

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.

In the Bushveld Complex, South Africa, open pit mines are faced with a challenge of rock slope stability due to geological structures (fractures, faults and dykes) that compartmentalize the rock mass. Geophysical surveys (seismics, magnetics and electrical methods) were conducted in a 0.2 km² area at Tharisa mine, with the goal to delineate fractures that may be potential conduits for water migration into the pit. Special processing techniques were applied to the dataset to obtain good quality seismic, magnetic and resistivity models. The P‐wave velocity models show distinct low velocities in the centre of the seismic profile, indicating the presence of weak zones associated with faulting or fracturing. Seismic reflection method was used to image the deeper discontinuities and mineralization contacts. Near surface reflections are observed throughout the profiles and are correlated with the contact between the chromitite and host rock. Ground magnetic surveys were conducted to delineate dykes and fractures. De‐trending and de‐culturing techniques were applied on the magnetic data for correcting regional and temporal variations. The low magnetic regions indicate the presence of fracture systems in the subsurface, whereas the high magnetic region is correlated with the dolerite dyke that crosscuts the pit. The electrical resistivity tomography exhibits linear low resistivity contrast zones that differentiate between the fractured and undisturbed hard rock at an estimated depth of 4–10 m. Resistivity shows discontinuities that suggests the presence of fracturing and dyke‐host rock contacts. Correlation among magnetics, P‐wave velocity models, resistivity section and seismic data is evident when overlaying the different datasets, implying that the low magnetic regions are highly weathered and prone to fracturing. The integration of geophysical data is encouraging, because it was able to image the depth to the bedrock, fractures within the host rock and dyke in a complex mining environment.

The Jiyang depression in the southeastern part of the Bohai Bay Basin has a relatively large scale set of shale oil in the Paleogene Shahejie Formation, but the complex internal components lead to narrow frequency bands, low resolution and difficulty in reservoir information extraction. Impedance is important information for reservoir characterization, and how to predict high‐resolution impedance using available information is particularly important. Deep learning, known for its effectiveness in addressing non‐linear problems, has found extensive applications in various fields of oil and gas exploration. However, the challenges of overfitting and poor generalization persist due to the limited availability of training datasets. Besides, existing methods often use networks to solve a single problem in fact, deep learning can deal with a series of problems intelligently. In order to partially solve the above problems, an intelligent storage prediction network framework is proposed in this paper. Physical information is introduced to realize data‐driven and model‐based approaches, thus solving the problem of difficult construction of training datasets. The processing part accomplishes the high‐resolution processing of seismic records, thus solving the problems of narrow bandwidth and low resolution. Initial model constraints are introduced so as to obtain more stable inversion results. Finally, the well data is compared and analysed to identify and predict the lithology and complete the intelligent prediction of unconventional reservoirs. The results are compared with the traditional model‐driven inversion method, revealing that the approach presented in this paper exhibits higher resolution in predicting dolomite. This contributes to the establishment of a robust data foundation for reservoir evaluation.

Particle swarm optimization, one of the modern global optimization methods, is attracting widespread interest because it overcomes the difficulties of conventional inversion techniques, such as trapping at a local minimum and/or initial model dependence. The main characteristic of particle swarm optimization is the large search space of parameters, which in a sense allows the exploration of the entire objective function space if the input parameters are properly chosen. However, in the case of a high‐dimensional model space, the numerical instability of the solution may increase and lead to unrealistic models and misinterpretations due to the sampling problem of particle swarm optimization. Therefore, smoothness‐constrained regularization techniques used for the objective function or model reduction techniques are required to stabilize the solution. However, weighting and combining objective function terms is partly a subjective process, as the regularization parameter is generally chosen based on some kind of criteria of how the smoothing constraints affect the data misfits. This means that it cannot be completely predefined but needs to be adjusted during the inversion process, which begins with the response of an initial model. In this paper, a new modelling approach is proposed to obtain a smoothness‐constrained model from magnetotelluric data utilizing multi‐objective particle swarm optimization based on the Pareto optimality approach without using a regularization parameter and combining several objective function terms. The presented approach was verified on synthetic models and an application with field data set from the Çanakkale–Tuzla geothermal field in Turkey. Findings from these analyses confirm the usefulness of the method as a new approach for all constrained inversions of geophysical data without the need to combine the objective function terms weighted by a regularization parameter.

We propose an approach for measuring seismic attenuation at ultrasonic frequencies on laboratory‐scale samples. We use a Gaussian filter to select a bandwidth of frequencies to identify the attenuation in a small window and, by moving the window across the frequency content of the data, we determine the frequency‐dependent attenuation function. We assess the validity of the method with three‐dimensional numerical simulations of seismic wave propagation across different sample geometries, using free surface boundary conditions. We perform the simulations using viscoelastic media under various seismic attenuation models. Our numerical results indicate that we can successfully recover the representative viscoelastic attenuation parameters of the media, regardless of the sample geometry, by processing the seismic signal recorded either within the volume or at the boundaries. Due to the equipartition phenomenon, the energy of S‐waves is consistently higher in seismic records than that of P‐waves. Therefore, we systematically recover the attenuating properties of S‐waves in the medium. We also conduct experiments of seismic wave propagation on samples of aluminum and Fontainebleau sandstone to validate our approach with real data. The quality factor of the S‐wave in the aluminum medium increases from 300 to 7000 between 60 kHz and 1.2 MHz. The Fontainebleau sandstone, which is more attenuating, exhibits a that increases from 200 at 60 kHz to 1000 at 1.2 MHz. With our approach, we are not only able to recover the attenuation property but also identify the frequency‐dependent attenuation model of the samples. Our method allows for seismic attenuation recovery at ultrasonic frequencies in low‐attenuating media.

Artificial caverns in salt rock formations play an important role in the net‐zero energy transition challenge, both for covering short‐term fluctuations in energy demand and serving as safe locations for long‐term underground gas storage both for hydrogen and natural gas. Geophysical tools can serve for monitoring geomechanical changes in the salt cavern during selection and development, and during gas storage/extraction activities, but the use of common geophysical monitoring techniques has been very limited in this area. Here, we present experimental work on physical and transport properties of halite rocks within the energy storage context and assess the potential of seismic and electromagnetic data to monitor gas storage activities in salt formations. First, we analysed the stress‐dependency of the elastic and transport properties of five halite rocks to improve our understanding on changes in the geological system during gas storage operations. Second, we conducted two dissolution tests, using cracked and intact halite samples, monitored with seismic (ultrasonic P‐ and S‐waves velocities and their attenuation factors) and electromagnetic (electrical resistivity) sources to evaluate (i) the use of these common geophysical sensing methods to remotely interpret caverning development and (ii) the effect of structural discontinuities on rock salt dissolution. Elastic properties and permeability showed an increasing trend towards rock sealing and mechanical enhancement with increasing pressure for permeabilities above 10⁻²¹ m², with strong linear correlations up to 20 MPa. In the dissolution tests, the ultrasonic waves and electrical resistivity showed that the presence of small structural discontinuities largely impacts the dissolution patterns. Our results indicate that seismic and electromagnetic methods might help in the selection and monitoring of the caverning process and gas storage operations, contributing to the expected increase in demand of large‐scale underground hydrogen storage.

Digital rock physics is a workflow that relies on imaging techniques to quickly and cost‐effectively estimate the petrophysical properties of small core samples taken from reservoirs. By using digital representations of rock samples as input, physics‐based simulators can estimate properties such as porosity and permeability. The accuracy of these estimates depends on the quality of the digital volumes generated from micro‐computed tomography scans. To enhance the accuracy, denoising is necessary to reduce image noise caused by various experimental factors like electronic noise and bad pixels. This study introduces a novel two‐step denoising pipeline that combines adaptive morphological filtering with non‐local means smoothing, ensuring both noise reduction and preservation of edges. The effectiveness of the proposed pipeline is assessed through qualitative evaluation using optimal segmentation results and quantitative evaluation using a non‐reference metric and equivalent number of looks. Comparing the results of the two‐step approach with traditional non‐local means and morphology‐based filtering using a multi‐resolution structurally varying bitonic filter, the non‐reference metric and equivalent number of looks values are higher, indicating improved denoising performance. Furthermore, the denoised rock volume is subjected to the next step in the digital rock workflow to compute important petrophysical properties like porosity and permeability. The findings indicate that our proposed pipeline significantly improves the accuracy of estimating physical parameters such as porosity and permeability.

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.

Seismic response from porous sediments can be used in reservoir characterization and fluid detection. Reflection coefficient at the isotropic/poroelastic interface is essential to reveal seismic response from the fluid‐saturated deposits. The exact normal incidence reflection coefficient is given by complex mathematic expression and not very clear relations with solid and fluid properties. Therefore, we derive the reflection coefficient approximate formula in series with respect to the square root of the imaginary unit multiplying by the ratio between angular frequency and the characteristic angular frequency. Compared to the exact reflection coefficient, the proposed approximate formula has a more concise mathematical form and clearer relationship with the moduli and densities. Meanwhile, there is no need to calculate the complex wavenumbers of poroelastic media from the dispersion equation as the exact reflection coefficient. When the frequency tends to zero, the approximate formula is consistent with reflection coefficient for the isotropic/isotropic interface. The proposed formula's applicability is verified through two models with an interface separating the mudstone and fully fluid‐saturated sandstone with pore fluids as water or carbon dioxide.