Estimation of shear wave velocity via rock physics-constrained KAN transformer

An evaluation of empirical and rock physics models to estimate shear wave velocity in a potential shale gas reservoir using wireline logs

Carbonate rocks have a wide variety of pore shapes and different types of grains, which greatly affect the elastic properties and characteristics of the reservoir. This causes certain difficulties in petroelastic modeling. One of the problems is the scale of the input data, which is then used to build the rock physics model. The paper presents the results of studying three core samples of carbonate rocks of the Upper Devonian and Lower Carboniferous age, which are located in the South Tatar arch (Volga-Ural oil and gas basin (Russia)). To evaluate the structural characteristics of the pore space, the effective medium theory is used. The input data are the results of laboratory studies that include measurements of the velocities of longitudinal and transverse waves, porosity, and thin section and computed tomography analysis. When using the computed tomography, the core samples are analyzed at different resolution (12–37 µm/voxel). The tomography studies of pore space at different scales provide rather different values of porosity and pore aspect ratio. The tomography-based porosity estimations also differ from the experimentally measured porosity (up to 10%). The pore space characteristics provided by different datasets are used to build a rock physics model for the studied rocks that helps to estimate the elastic wave velocities with three different methods of effective medium theory (self-consistent approximation, differential effective medium (DEM), and the Kuster–Toksöz method). A comparison of the velocity estimations with their experimental analogs for dry rocks may indicate the presence of microcracks whose size is beyond the tomography resolution. Improved rock physics models incorporating both pores and microcracks are then used to predict the elastic wave velocities of fluid-saturated rock in a wide porosity range. It is demonstrated that the predicted values significantly differ (up to 30%) from those provided by the rock physics (RP) models constructed without the support of the tomography results. Moreover, other types of models are considered in which the difference in experimental and theoretical velocities is attributed to changes in the host matrix properties as compared to the calcite polycrystal, which are caused by various reasons.

The deep carbonate reservoirs in the Tarim Basin, Xinjiang, China, are widely developed with multi-scale complex reservoir spaces such as fractures, pores, and karst caves under the coupling of abnormal high pressure, diagenesis, karst, and tectonics and have strong heterogeneity. Among them, fracture–cavity carbonate reservoirs are one of the main reservoir types. Revealing the petrophysical characteristics of fracture–cavity carbonate reservoirs can provide a theoretical basis for the log interpretation and geophysical prediction of deep reservoirs, which holds significant implications for deep hydrocarbon exploration and production. In this study, based on the mineral composition and complex pore structure of carbonate rocks in the Tarim Basin, we comprehensively applied classical petrophysical models, including Voigt–Reuss–Hill, DEM (Differential Effective Medium), Hudson, Wood, and Gassmann, to establish a fracture–cavity petrophysical model tailored to the target block. This model effectively characterizes the complex pore structure of deep carbonate rocks and addresses the applicability limitations of conventional models in heterogeneous reservoirs. The discrepancies between the model-predicted elastic moduli, longitudinal and shear wave velocities (Vp and Vs), and laboratory measurements are within 4%, validating the model’s reliability. Petrophysical template analysis demonstrates that P-wave impedance (Ip) and the Vp/Vs ratio increase with water saturation but decrease with fracture density. A higher fracture density amplifies the fluid effect on the elastic properties of reservoir samples. The Vp/Vs ratio is more sensitive to pore fluids than to fractures, whereas Ip is more sensitive to fracture density. Regions with higher fracture and pore development exhibit greater hydrocarbon storage potential. Therefore, this petrophysical model and its quantitative templates can provide theoretical and technical support for predicting geological sweet spots in deep carbonate reservoirs.

Summary A novel method for Total Organic Carbon (TOC) content prediction was proposed utilizing pre-stack elastic impedance inversion. First of all, a new rock physics model was established considering mineral compositions, pore type and fluid content according to the elastic property experiment of shale gas core sample. S wave velocity curve was estimated by the rock physics modelling and the elastic parameters were calculated by P wave velocity, S wave velocity and density. Then, the pre-stack super gather was pre-processed for obtaining high quality angle gather. We derived P wave velocity, S wave velocity and density through pre-stack elastic impedance inversion in framework of Bayes theory under the constraint of prior information such as well logging data, geology knowledge. Finally, under the direction of rock physics analysis, the relationships between TOC content and elastic parameters were established. We find that P wave velocity to S wave velocity ratio had a good relationship with TOC content and we get a high accurate prediction of TOC content. Real data example test showed that TOC content predicted result agreed with geology property and well log interpretation result. The new approach can offer a reliable geophysical evidence for TOC evalutaion.

Carbonate rock are important hydrocarbon reservoir rocks with complex texture and petrophysical properties (porosity and permeability). These complexities make the prediction reservoir characteristics (e.g. porosity and permeability) from their seismic properties more difficult. The goal of this paper are to understanding the relationship of physical properties and to see the signature carbonate initial rock and shally-carbonate rock from the reservoir.
 To understand the relationship between the seismic, petrophysical and geological properties, we used rock physics modeling from ultrasonic P- and S- wave velocity that measured from log data. The measurements obtained from carbonate reservoir field (gas production). X-ray diffraction and scanning electron microscope studies shown the reservoir rock are contain wackestone-packstone content. Effective medium theory to rock physics modeling are using Voigt, Reuss, and Hill.
 It is shown the elastic moduly proposionally decrease with increasing porosity. Elastic properties and wave velocity are decreasing proporsionally with increasing porosity and shally cemented on the carbonate rock give higher elastic properties than initial carbonate non-cemented. Rock physics modeling can separated zones which rich of shale and less of shale.

ABSTRACTThe elastic properties of argillaceous sandstones are significantly controlled, however, by the perplexing distribution of dispersed, cemented and matrix (including structural and layered) clay. The corresponding rock physics models are established to investigate the influence of different clay distribution on the elastic properties of sandstone. The rock physics modelling and laboratory experimental results exhibit that the higher the content of the matrix clay, the lower the elastic wave velocity. The dispersed and cemented clay increases the sandstone's velocity by reducing the porosity; the increase of dispersed clay only causes a slight increase in the elastic wave velocity in sandstone. In contrast, a small amount of cemented clay can significantly increase the velocity in sandstone. Based on these understandings, we construct a cross‐plot of P‐wave velocity and clay content as a template to diagnose clay distribution. Based on the rock physics model and empirical knowledge, we divide the template into four zones, namely, matrix, dispersed, cemented and mixed clay distribution zones. Then, we use the published experimental data and numerical modelling data to validate the template. Based on diagnosing the clay distribution, we introduce how to use the diagnostic results of the template to select a suitable rock physics model for argillaceous sandstone in Shengli Oil Field, East China. The selected rock physics model, guided by diagnosing the clay distribution, predicts P‐ and S‐wave velocity very well. The proposed rock physics template for diagnosing clay distribution also has potential application in well‐logging data interpretation, rock physics modelling and reservoir characterization.

Conversion of building properties is performed primarily to reduce the compressibility of the base and increase strength. However, in addition to changing the static properties of soils, it is necessary to take into account changes in dynamic properties, such as dynamic shear modulus, damping coefficient, velocity of elastic shear and longitudinal waves. Thus, it is possible to control the dynamic properties of the base by converting the construction properties of soils. In this paper, we consider the change in the velocity of shear waves in a base transformed by the technology of deep soil mixing (DSM). In the work, the influence of additional vertical stresses on the dynamic properties of the bases transformed using deep soil mixing technology was evaluated. As a parameter that allowed us to evaluate the dynamic properties, the propagation velocity of elastic shear waves was chosen. Shear wave velocity was estimated based on the results of triaxial tests using the method of low-amplitude torsional vibrations in a resonant column. The research results showed that at small values of axial strains, there is no significant increase in the velocity of shear waves, an increase in the speed of shear waves by two times with an increase in stress by 0.6 MPa.

Theoretical relationship between elastic wave velocity and electrical resistivity

Shear wave velocity prediction: A review of recent progress and future opportunities

Summary Our study applied a geophysical well log analysis, rock physics diagnostics and rock physics modelling to an exploration well log data from a shale gas exploration area in the Sichuan Basin o f South China. The study established an unconsolidated model (80% quartz plus 20% clay in the shale gas for mation) transform between the acoustic and elastic impedance on the one hand and lithology, porosity, water saturation, clay content, quartz content, and TOC content on the other hand. Through our geophysical well log anal ysis, we calculated mineral volumes using best available data, total and effective porosity, water saturation, and bu lk density and VS prediction where it was missing. For rock physics modeling, the shale gas formation matr ix substitution (Clay, Quartz and TOC) and porosity modeling were performed in this exploration well. Crossplots ar e also used to analyze the elastic properties of the shale gas formation including VP velocity vs density, Acoustic Impedance ( AI) vs total porosity (ΦT), AI vs Poisson’s Ratio (P R), and VP vs VS. The results were quality controlled by core sample laboratory analysis data. T o understand seismic effect as a result of rock physics modeling, ray tr aced synthetic modelling has been applied. The Ray-traced synthetics have been generated for the in situ and modeled scen arios for AVA analysis. These transforms will be upscaled and applied to acoustic and elastic impedance inversion volum es to map lithology, porosity, and TOC distribution in the shale gas exploration area.

4D reservoir characterization using well log data for feasible CO2-enhanced oil recovery at Ankleshwar, Cambay Basin - A rock physics diagnostic and modeling approach

Our study applied a geophysical well log analysis, rock physics diagnostics and rock physics modelling to an exploration well log data from a shale gas exploration area in the Sichuan Basin of South China. The study established an unconsolidated model (80% quartz plus 20% clay in the shale gas formation) transform between the acoustic and elastic impedance on the one hand and lithology, porosity, water saturation, clay content, quartz content, and TOC content on the other hand. Through our geophysical well log analysis, we calculated mineral volumes using best available data, total and effective porosity, water saturation, and bulk density and VS prediction where it was missing. For rock physics modeling, the shale gas formation matrix substitution (Clay, Quarzt and TOC) and porosity modeling were performed in this exploration well. Crossplots are also used to analyse the elastic properties of the shale gas formation including VP velocity vs density, Acoustic Impedance (AI) vs total porosity (ΦT), AI vs Poisson's Ratio (PR), and VP vs VS. The results were quality controlled by core sample laboratory analysis data. To understand seismic effect as a result of rock physics modeling, ray traced synthetic modelling will be applied. The Ray-traced synthetics will be generated for the in situ and modeled scenarios for future AVA analysis. These transforms will be upscaled and applied to acoustic and elastic impedance inversion volumes to map lithology, porosity, and TOC distribution in the shale gas exploration area.

Shear-wave velocity prediction of tight reservoirs based on poroelasticity theory: A comparative study of deep neural network and rock physics model

Formation evaluation is one of the essential elements in hydrocarbon exploration. Rock physics is one of the key components in exploration, development, and hydrocarbon production in which it provides correlation between geologic reservoir parameters and seismic properties. The aim of this study is to analyze how clay mineral content can affect the Amplitude Versus Offset (AVO) seismic response on sandstone reservoir based on the Gassmann equation in rock physics modeling. In this study, rock physics modeling was conducted from two wells data (X-1 zone C and X-2 zone Q) in Balikpapan Formation, Kutai Basin, with a mixture of clay and quartz using Gassmann equation. The rock physics modeling process consisted of modeling the minerals, the fluids, as well as the rock frame. Those three were assembled with Gassmann equation to calculate Velocity-P (Vp). Velocity-S (Vs), and Density Bulk (ρb). Acoustic impedance (AI) and shear impedance (SI) were calculated to get R (θ) values from Zoeppritz approximation. With the seismic forward modeling, the synthetic seismogram was produced by using R (θ) values from modeling to know the AVO seismic response. In conclusion, our experiment showed that zone C in well X-1, with Illite as clay content, has higher modeling values compared to real data. While zone Q in well X-2, with Smectite as clay content, has lower modeling values compared to real data. Thus, the R (θ) values from rock physics modeling in zone Q gave more negative impedance contrast which affect in clearer bright spot on AVO seismic response, compared to real data. While in zone C, the R (θ) values from rock physics modeling did not gave insignificant change in impedance contrast so that the AVO seismic response were not affected.

Formation evaluation, and specifically hydrocarbon volume estimations, are tightly dependent on the rock physics model (RPM) used for the interpretation of well logs and core data. The latter models are known to exhibit small but significant variations throughout multiple wells located in the same hydrocarbon field. To improve the accuracy and reliability of the interpretation, the RPMs are typically adjusted ad-hoc. We automate the multiwell interpretation process by relying on local petrophysical inversion of well logs and core data. A spatial correlation function is used to implement the RPMs, both vertically and laterally. In addition to improving formation evaluation in each well, our inversion-based method mitigates layer-boundary, geometrical, and instrument-related effects on well logs and identifies data outliers and measurement imbalances where further quality control might be needed. First, we invert each available well log into an equivalent physical property represented by a layer-by-layer blocky log with an associated uncertainty (earth model: piecewise constant layers with discontinuities at layer boundaries). This mitigates any tool, shoulder-bed, or borehole-condition dependency. Then, we use the extra measurements (well logs and core data) from a key well to determine an initial RPM (e.g., Juhasz parameters and density of minerals), as well as probabilistic prior distributions for all properties, e.g., porosity and water saturation. Next, we propagate the RPM and prior distributions throughout the field using Bayesian petrophysical/compositional joint inversion (PJI) for all petrophysical properties in every well, concomitantly propagating uncertainties to petrophysical/compositional properties. With each non-key well having a full set of physical (from well logs) and petrophysical/compositional properties, we generate new priors and RPMs for each well by minimizing the PJI misfit. These new priors and RPMs are used to further refine priors, and RPMs on neighboring wells. We enforce consistency via spatial variograms for RPMs. The process is repeated iteratively while tightening the variogram until no further improvement is possible. This method guarantees that the variation of RPMs is consistent across spatial correlations. The accuracy of the method is improved as more field data are available to corroborate and refine local RPMs and prior distributions. By using adaptive RPMs over tool and borehole-condition-mitigated layer properties, we were able to match core data constituted by porosity, fluid saturations, and mineral composition. Our results replicated 87% of the core data within the 95% confidence interval; in contrast, using a universal RPM replicates a lower 80% of the core data within the 95% confidence interval. Traditional interpretation methods cannot capture confidence intervals and yield significantly poorer matches in all properties; when comparing specifically hydrocarbon pore volume, our method shows an average 5% accuracy improvement. We generalized a logging tool and borehole-condition-independent Bayesian inference petrophysical estimation method to a multiwell framework. By considering the entire hydrocarbon field as a single petrophysical joint inversion of well logs and core data, we increased the accuracy of formation evaluation and/or identified outliers or data imbalances that signaled poor or biased data that required further quality control.