Rock Physics Model Research Articles

Impacts of solid rock components on resistivity-based reserves evaluation in organic-rich mudrocks

The effective electrical conductivity of organic-rich mudrocks can be influenced by the presence of clay, pyrite, and thermally mature organic matter. In this paper, we investigate the impacts of conductivity, volumetric concentrations, and spatial distribution/connectivity of clay, pyrite, and kerogen networks on the effective conductivity of the rock and how this affects the assessment of water/hydrocarbon saturation. This quantification enables an understanding of when such components need to be considered in interpreting resistivity measurements in organic-rich mudrocks. We perform sensitivity analysis on the impacts of (a) thermal-maturity-dependent kerogen conductivity, (b) volumetric concentration/spatial distribution of kerogen, (c) conductivity and volumetric concentration/spatial distribution of pyrite, and (d) cation exchange capacities of various clay minerals and their concentration/spatial distribution on the effective conductivity of the rock. The sensitivity analysis showed that with increasing pyrite concentration, the effective conductivity of the rock increased. This increment became significant when the concentration of pyrite was above 2%. Lastly, we observed that thermally mature kerogen increased the conductivity of the rock, with a significant increment occurring when the kerogen conductivity was above 0.01 S m −1 . Results confirmed that the incorporation of conductive rock components such as clay, pyrite, and kerogen into the electrical rock physics model improved the estimates of hydrocarbon reserves.

Deep Dive into the factors influencing acoustic velocity in the Dalan-Kangan formations, the central Persian Gulf

The propagation of acoustic waves in rocks is directly influenced by the rocks’ elastic properties. To evaluate petrophysical properties from acoustic data, it is essential to understand the factors that influence the variations in acoustic velocity of sedimentary rocks. Carbonate reservoirs are heterogeneous, and their evaluation is complicated. A closer look at the factors affecting the elastic properties of these rocks is essential because they reveal important information about the reservoir properties and their formation evaluation. Rock physics modeling relies on laboratory-measured acoustic data. This study, however, employs acoustic logs to elucidate the key factors influencing sonic velocity, offering a more comprehensive and field-applicable perspective. Samples were prepared from the Permian–Triassic Upper Dalan and Kangan formations. Porosity, permeability, sedimentary textures, mineralogy, and pore types were investigated. Furthermore, compressional, shear, and Stoneley well-logs data were recorded and converted to acoustic velocity in the sampled interval. Porosity-velocity modeling based on the differential effective medium (DEM) theory was performed for different values of equivalent pore aspect ratio (EPAR). The results show that there is a strong inverse relationship between acoustic velocities and porosity. Acoustic velocities also exhibit an inverse correlation with permeability, but the strength of these relationships is relatively weak. Acoustic velocities are not affected by carbonate textures. A specific texture may exhibit different velocities due to the influence of diagenetic processes. Mineralogy has only a minor influence on acoustic velocities. Dolostones have a slightly higher velocity than limestones, but the difference is insignificant. Stoneley log values are slightly different in limestone and dolostone, because this wave propagates along the mud-borehole interface. The velocities were affected by the variations in pore types within the studied formations. Therefore, in carbonate rocks, porosity, and the pore types play a significant role in controlling acoustic velocities. The value of EPAR = 0.15 can be considered as the boundary between stiff pores (moldic and vuggy) and soft pores (interparticle, intercrystalline, micro-intercrystalline, and microporosity). The lower EPAR value of interparticle pores in limestones compared to dolostones can be attributed to diagenetic processes such as dolomitization and compaction. The findings of this study can help to better understand what factors affect acoustic velocity in carbonate rocks. This study also shows that acoustic logs data can be effectively used in rock physics modeling and pore type identification. By identifying the pore types, it is possible to recognize diagenetic processes and determine the rock types to control the heterogeneity and estimate reservoir quality.

Using seismic petrophysical modeling and prestack simultaneous inversion to provide insights into the physical properties of uranium-bearing reservoirs: Implications for favorable sites of sandstone-hosted uranium deposits

Seismic prospecting has been accepted as one of the most widely available methods for exploring sandstone-hosted uranium deposits (SUDs). However, conventional seismic interpretation faces a challenge in the identification and characterization of a uranium reservoir’s complexity. How to characterize in detail a uranium reservoir’s physical complexity and effectively improve uranium reservoir prediction accuracy remain unresolved problems. To address this, we develop a novel combination of petrophysical modeling and prestack simultaneous inversion to understand in detail the physical properties of uranium-bearing reservoirs and efficiently predict favorable SUD sites. First, we develop a workflow of rock-physics modeling for SUD logs using the Xu-White method to calculate the modulus of elasticity of the grain matrix; subsequently, we extend the Walton model for the modulus prediction of the dry rocks and the Gassmann equation for one of the saturated rocks after a massive calculation test; and then, we predict the S-wave data used for the following inversion. Second, we execute a prestack simultaneous inversion to obtain the petrophysical parameters (e.g., P-impedance, density [[Formula: see text]], shear modulus [[Formula: see text]], Lamé coefficient [[Formula: see text]], and Young’s modulus) that can provide insights into the physical properties of a uranium metallogenic environment. Accordingly, we discover that sites bearing uranium mineralization strongly correspond to areas with low elastic-parameter values (especially [Formula: see text] and [Formula: see text]), whereas nonuranium anomalies occur in high-value sites. This indicates that weakened elastic characteristics are caused by the enhancement of the total organic content and total clay mineral volumes of the uranium-bearing layers. In summary, the developed combination approach can yield an effective and accurate characterization of the geologic properties of uranium-bearing formations, and it can provide prediction factors (e.g., parameters related to the shear modulus) for uranium mineralization.

Five-dimensional facies-driven seismic inversion for igneous reservoirs based on rock-physics modelling

Abstract The igneous reservoir has become an important exploration target for increasing reserves and production of oil and gas in the Junggar Basin. However, igneous reservoir exploration is restricted because the seismic exploration of high-quality igneous reservoirs is difficult and the anisotropy induced by high-angle fractures cannot be neglected. To implement the characterization of igneous reservoirs, we first study the correlation between anisotropy parameters and physical properties of igneous rock, and we propose a 5D facies-driven inversion method based on rock physics, which means we employ 3D seismic data at different incidence angles and azimuths to implement the estimation of a hydrocarbon reservoir constrained by the igneous rock facies. We also present an anisotropic igneous rock-physics model, in which micro-petrophysical characteristics, strong heterogeneity of skeleton minerals, and pore structures are considered. As a reasonable initial model is important for seismic inversion, we propose a facies-driven modelling seismic inversion method in which we use facies obtained on the basis of the difference between rock composition, reservoir physical parameters, and elastic parameters of different lithofacies igneous rocks to constrain the seismic inversion. Finally, we present a step seismic inversion method of using seismic data to estimate multi-parameters of horizontal transversely isotropic media. Therefore, the comprehensive processes of rock-physics modelling, inversion model establishment, and reservoir prediction of high-quality igneous rocks are proposed in this study, which demonstrates effective application for igneous reservoirs in China.

Prediction of reservoir properties using inverse rock physics modelling in the Kanywataba Exploration Area, Albertine Graben

In this study we used the concept of inverse rock physics modelling to analyse reservoir properties of the Kanywataba Exploration Area, with a focus on their lateral distribution away from the Kanywataba well. The procedure employed rock physics models calibrated for the basin constrained by seismic inversion data, where non-uniqueness and data error propagation issues were also taken into account. Both seismic and well log datasets were used in the data calibration. The procedures enabled us to obtain the most likely estimate mean, weighted mean and posterior mean of the reservoir properties. We obtained a good match between measured and modelled porosity values. Any misfit between the observed and predicted lithology was mainly attributed to uncertainties in defining the correct mineral properties. The integrated approach revealed that high porosities correlate with low clay volumes and, furthermore, indicated two distinct reservoir units in the basin, which were interpreted as the Oluka and Kakara Formations. Fluid saturation data were less successfully predicted but this was most probably due to a result of lack of real saturation logs for use in the calibration of the rock physics model; instead, predicted saturation logs based on Archie's law were used in the calibration process. This analysis is first of its kind in this basin and therefore exhibits a high level of novelty in the determination of reservoir properties in this area.

Quantifying the influence of clay-bound water on wave dispersion and attenuation signatures of shale: An experimental study

Understanding the elastic and attenuation signatures of shales is of considerable interest for unconventional reservoir characterization and sealing capacity evaluation for CO2 sequestration and nuclear waste disposal. We have conducted laboratory measurements on seven shale samples at seismic frequencies (2–100 Hz) to study the effects of clay-bound water (CBW) on their wave dispersion and attenuation signatures. With nuclear magnetic resonance and a helium porosimeter, the volume of CBW in the shale samples is quantified. The forced-oscillation measurement reveals that Young’s modulus exhibits a continuous dispersion trend from 2 to 100 Hz. The extensional attenuation ([Formula: see text]) shows a weak frequency and pressure dependence on effective pressure ranging from 5 to 35 MPa. The magnitude of extensional attenuation shows a positive correlation with CBW, with an [Formula: see text] value of 0.89. It is found that 4% of CBW in the rock frame causes approximately a 5% modulus increase from 2 to 100 Hz. We adopt a constant [Formula: see text] model for assigning frequency-dependent bulk and shear moduli to the CBW in the rock-physics modeling, which can fit the experimental data of modulus dispersion and attenuation well, indicating that the bulk and shear moduli of CBW in shales might behave viscoelastically.

Advancing Quantitative Seismic Characterization of Physical and Anisotropic Properties in Shale Gas Reservoirs with an FCNN Framework Based on Dynamic Adaptive Rock Physics Modeling

Quantitative seismic methods are crucial for understanding shale gas reservoirs. This study introduces a dynamic adaptive rock physics model (DARPM) designed to systematically quantify the relationship between physical parameters and elastic parameters within shale formations. The DARPM uniquely adapts to changes in formation dip angle, allowing adaptive reservoir property assessment. An innovative adaptive rock physics inversion methodology is subsequently proposed to compute values for reservoir physical and seismic anisotropy parameters. This is achieved using well log data and building upon the foundation laid by the established DARPM. We introduce the RPM-FCNN (rock physics model—fully connected neural network) framework, seamlessly integrating the DARPM with the corresponding inversion results into a comprehensive model. This framework facilitates a quantitative analysis of the nonlinear relationship between elastic and reservoir physical parameters. Utilizing the trained RPM-FCNN framework, the spatial distribution of reservoir and seismic anisotropic characteristics can be precisely characterized. Within this framework, the organic matter mixture aspect ratio indicates the continuity of organic matter, while the organic matter porosity reveals the maturity of organic matter. Simultaneously, seismic anisotropy characteristics signify the degree of stratification within the reservoirs. This method, therefore, establishes a robust foundation for identifying favorable areas within shale gas reservoirs.

Sandstone reservoir rock physics modeling and time-lapse seismic analysis

Time-lapse seismic analysis can be used to monitor changes in reservoir parameters over time as the development progresses. The physical parameters of reservoir, such as porosity and water saturation, will undergo changes with the development of the oil field. Theoretical rock physics models establish the relationship between reservoir petrophysical parameters and reservoir elastic parameters. We propose a petrophysical parameter substitution method based on variable critical porosity rock physics model to further analyze the impact of changes in reservoir petrophysical parameters on the elastic parameters (P-wave velocity, S-wave velocity and density) of the reservoir. Porosity substitution and fluid substitution can accurately characterize the changes in the elastic parameters of rocks with variations in petrophysical parameters. The method of controlling variables is employed during rock physics substitution, when substituting one type of petrophysical parameter, the other petrophysical parameters should remain unchanged. By using rock physics inversion, it is possible to invert the critical porosity from velocities of reservoir. Afterward, we substitute petrophysical parameters: porosity and water saturation, by comparing and analyzing the seismic wave velocities and seismic characteristics before and after substitution, we can analyze the impact of changes in reservoir petrophysical parameters on seismic. The test results from theoretical model and well logging data indicate that the rock physics substitution method can be applied to differentiate the influences of reservoir petrophysical parameters such as porosity and water saturation on reservoir elastic parameters and seismic characteristics.

Petrophysical Property Prediction from Seismic Inversion Attributes Using Rock Physics and Machine Learning: Volve Field, North Sea

An accurate petrophysical model of the subsurface is essential for resource development and CO2 sequestration. We present a new workflow that provides a high-resolution estimate of petrophysical reservoir properties using seismic data with rock physics modeling and machine-learning techniques (i.e., deep learning neural networks). First, we compare the sequential prediction of the following petrophysical attributes: mineralogy, porosity, and fluid saturation, with the simultaneous prediction of all of the properties using the Volve field in the Norwegian North Sea as an example. The workflow shows that the sequential prediction produces a more efficient and accurate classification of petrophysical properties (the RMS error between the predicted and the original seismic trace is 50% smaller for the sequential compared to the simultaneous procedure). Next, the seismic amplitude response of the reservoirs was studied using rock physics modeling and amplitude versus offset (AVO) analysis to distinguish the different lithologies and fluid types. To ascertain the optimal hydrocarbon production areas, we performed Bayesian seismic inversion and applied machine learning to estimate the petrophysical properties. We examined how porosity, Vclay, and fluid variations affect the elastic properties. In Poisson’s ratio versus the P-wave impedance domain, a 10% porosity increase decreases the acoustic impedance (AI) by 30%, while a 20% Vclay decrease increases the AI by 12%. The Utsira Formation in the Volve field (5 km north of the Sleipner Øst field) was evaluated as a potential CO2 geological storage unit using Gassmann fluid substitution and seismic modeling. We look to assess the elastic property variation caused by CO2 saturation changes for monitoring purposes and simulate the effect. In the first 10% CO2 substitution, the P-wave velocity decrease is 12%, a subtle effect is observed for higher CO2 saturation values, and S-wave velocity (Vs) increases with CO2 saturation. Our analysis aspires to assist future reservoir studies and CO2 sequestration in similar fields.

Integrated Petrophysical Evaluation and Rock Physics Modeling of Broom Creek Deep Saline Aquifer for Geological CO2 Storage

Fossil fuels, such as coal and hydrocarbons, are major drivers of global warming and are primarily responsible for worldwide greenhouse gas emissions, including carbon dioxide CO2. The storage of CO2 in deep saline reservoirs is acknowledged as one of the top practical and promising methods to reduce CO2 emissions and meet climate goals. The North Dakota Industrial Commission (NDIC) recently approved the fourth Class VI permit for a carbon capture and storage project in the Williston basin of North Dakota for the geological CO2 storage in the Broom Creek formation. The current research aimed to conduct a comprehensive petrophysical characterization and rock physics modeling of the Broom Creek deep saline reservoir to unravel the mineralogical distribution and to understand the variations in petrophysical and elastic properties across the formation. This study utilized geophysical well logs, routine core analysis, and advanced core analysis to evaluate the Broom Creek formation. Multimineral petrophysical analysis calibrated with X-ray diffraction results reveals that this formation primarily comprises highly porous clean sandstone intervals with low-porosity interspersed with dolomite, anhydrite, and silt/clay layers. The formation exhibits varying porosities up to 0.3 and Klinkenberg air permeabilities up to ∼2600 mD. The formation water resistivity using Archie’s equation is approximately 0.055 ohm-m at 150 °F, corresponding to around 63,000 ppm NaCl salinity, which is consistent with prior data. The pore throat distribution in the samples from clean sandstone intervals is primarily situated in the macro-mega scales. However, the presence of anhydrite and dolomite impedes both porosity and pore throat sizes. The accurate prediction of effective elastic properties was achieved by developing a rock physics template. Dry rock moduli were modeled using Hill’s average, while Berryman’s self-consistent scheme was employed for modeling saturated moduli.

Quantitative characterization of organic and inorganic pores in shale based on deep learning

Organic matter (OM) maturity is closely related to organic pores in shales. Quantitative characterization of organic and inorganic pores in shale is crucial for rock-physics modeling and reservoir porosity and permeability evaluation. Focused ion beam-scanning electron microscopy (FIB-SEM) can capture high-precision three-dimensional (3D) images and directly describe the types, shapes, and spatial distribution of pores in shale gas reservoirs. However, due to the high scanning cost, wide 3D view field, and complex microstructure of FIB-SEM, more efficient segmentation for the FIB-SEM images is required. For this purpose, a multiphase segmentation workflow in conjunction with a U-net is developed to segment pores from the matrix and distinguish organic pores from inorganic pores simultaneously in the entire 3D image stack. The workflow is repeated for FIB-SEM data sets of 17 organic-rich shales with various characteristics. The analysis focuses on improving the efficiency and relevance of the workflow, that is, quantifying the minimum number of training slices while ensuring accuracy and further combining the fractal dimension (FD) and lacunarity to study a simple and objective method of selection. Meanwhile, the computational efficiency, accuracy, and robustness to noise of the 2D U-net model are discussed. The intersection over the union of automatic segmentation can amount to 80%–95% in all data sets with manual labels as ground truth. In addition, calculated by the FIB-SEM multiphase segmentation, the organic porosity is used to quantitatively evaluate the OM decomposition level. Deep-learning-based segmentation shows great potential for characterizing shale pore structures and quantifying OM maturity.

Estimating the anisotropy of the vertical transverse isotropy coal seam by rock physics model–based inversion

AbstractCoal 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 attribute transformation and porosity prediction of thin water‐rich sandstone based on Lambert W–R model

AbstractThe 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.

Experimental Study of Seismic Wave Attenuation in Carbonate Rocks

Summary Seismic wave attenuation has a great potential for studying saturated and fractured media, due to its high sensitivity to the physical properties of geological media. However, accurately estimating this parameter can be challenging due to its sensitivity to signal noise, particularly in heterogeneous media such as carbonate rocks. This explains the paucity of attenuation studies carried out in carbonate rocks compared with sandstones, and the ambiguity around its mechanisms and its relationship with petrophysical properties. To investigate further, we conducted an experimental study of ultrasonic waveform signals (0.5–3 MHz) reordered under dry and fully saturation conditions in 13 samples covering a wide range of petrophysical values and subjected them to differential pressure reaching reservoir pressure. The resulting increase in attenuation magnitudes and their variation with pressure due to brine saturation were more pronounced than in velocity magnitudes, confirming the higher sensitivity of attenuation to fluid content. However, understanding the relationship between attenuation and petrophysical properties required a careful examination of the results and more elucidation about attenuation mechanisms. We suggested that multiple attenuation mechanisms coexist, including scattering, cracks slipping, solid frictional relative motion, and global and squirt flow. This explains the frequency dependence of attenuation, with higher magnitudes at sonic frequencies, where the squirt flow mechanism may be dominant. In contrast to sandstone, the magnitude of compressional to shear attenuation ratio (QP−1/QS−1) was found to be greater than unity in both dry and brine fully saturated carbonate samples at ultrasonic frequencies. This result may be due to the complex porosity structure of carbonate rocks, which makes it not appropriate to the sandstone rock physics models.

Introducing a simplified rock physics model to estimate shear velocity to consider the geometry of pore spaces and minerals

Introducing a simplified rock physics model to estimate shear velocity to consider the geometry of pore spaces and minerals

Quantitative prediction of the fracture scale based on frequency-dependent S-wave splitting

The development of natural fractures has a significant impact on underground reservoirs and leads to seismic anisotropy. Furthermore, the scale of natural fractures directly affects oil and gas preservation, hydraulic fracture construction, and the production development of shale reservoirs. The S-wave anisotropy is a frequency-dependent parameter and the change in S-wave anisotropy with frequency is a function of the fracture scale. We develop an innovative method for predicting the fracture scale quantitatively using frequency-dependent S-wave anisotropy. The quantitative relationship between different fracture scales and the frequency-dependent response of the S-wave splitting (SWS) anisotropy can be obtained using a dynamic rock-physics model. The frequency-dependent S-wave anisotropy is calculated via SWS analysis in the frequency domain, after which this quantitative relationship and the calculated frequency-dependent response are used to establish an objective function for the inversion of the fracture scale at different depths using the least-squares algorithm. We synthesize data under ideal conditions, test our method, apply our method to field data, and find that the quantitative prediction method of the fracture scale yielded reasonable prediction results. The S-wave anisotropy is calculated based on the SWS analysis from the horizontal components of the upgoing wavefields of the field vertical seismic profile. We compare the fracture scale calculated from logging data using our method, and the results obtained indicate that this method can successfully predict the fracture scale quantitatively.

Probabilistic physics-informed neural network for seismic petrophysical inversion

The main challenge in the inversion of seismic data to predict the petrophysical properties of hydrocarbon-saturated rocks is that the physical relations that link the data to the model properties often are nonlinear and the solution of the inverse problem is generally not unique. As a possible alternative to traditional stochastic optimization methods, we develop a method to adopt machine-learning algorithms by estimating relations between data and unknown variables from a training data set with limited computational cost. We develop a probabilistic approach for seismic petrophysical inversion based on physics-informed neural network (PINN) with a reparameterization network. The novelty of our approach includes the definition of a PINN algorithm in a probabilistic setting, the use of an additional neural network (NN) for rock-physics model hyperparameter estimation, and the implementation of approximate Bayesian computation to quantify the model uncertainty. The reparameterization network allows us to include unknown model parameters, such as rock-physics model hyperparameters. Our method predicts the most likely model of petrophysical variables based on the input seismic data set and the training data set and provides a quantification of the uncertainty of the model. The method is scalable and can be adapted to various geophysical inverse problems. We test the inversion on a North Sea data set with poststack and prestack data to obtain the prediction of petrophysical properties. Compared with regular NNs, the predictions of our method indicate higher accuracy in the predicted results and allow us to quantify the posterior uncertainty.

Mechanical behaviour and microstructure of methane hydrate-bearing sandy sediment observed at various spatial scales

Methane hydrates (MHs) are considered an alternative energy resource but also a potential source of geo-hazards and climate change. The physical/mechanical properties of gas hydrate-bearing sandy sediments are strongly dependent on the distribution of hydrates within the pore space. The purpose of this study is to investigate morphologies and pore-habits of MHs formed in sandy sediments by means of experiments that probe a wide range of scales, from the pore scale – using Synchrotron X-Ray Computed Tomography (SXRCT) and optical microscopy – to the core scale, through mechanical property measurements. The same synthetic sands are used, in which MHs are generated successively under excess gas and excess water conditions. At the macroscopic (core) scale, MH pore habits are inferred by comparing the measured sonic wave velocities to velocities calculated from rock physics models and further assessed via triaxial compression tests. Furthermore, Magnetic Resonance Imaging is used to investigate the kinetics of MH formation and distribution along the core height. The pore habits and MH morphologies are directly visualized at the pore (grain) scale by SXRCT and, with still better spatial and temporal resolution, by transmission optical microscopy, revealing some more complex morphologies than in the hydrate pore habits commonly admitted.

Rock-physics model of a gas hydrate reservoir with mixed occurrence states

Seismic interpretation of gas hydrates requires the assistance of rock physics. Changes in gas hydrate saturation can alter the elastic properties of formations, and this relationship can be considerably influenced by the occurrence state of gas hydrates. Pore-filling, load-bearing, and cementing types are three single gas hydrate occurrence states commonly considered in rock-physics investigations. However, many gas hydrate-bearing formations are observed to have mixed occurrence states, and their rock-physics properties do not fully conform to models of single occurrence states. We develop a generalized rock-physics model for gas hydrate-bearing formations with three mixed occurrence states observed in the field or laboratory experiments: coexisting pore-filling-type and matrix-forming-type gas hydrates (case 1); pore-filling type when [Formula: see text] (gas hydrate saturation) < [Formula: see text] (critical saturation) and pore-filling + matrix-forming type when [Formula: see text] (case 2); and matrix-forming type when [Formula: see text] and matrix-forming + pore-filling type when [Formula: see text] (case 3). Instead of initial porosity, the apparent porosity (the volume fraction of an effective pore filler) [Formula: see text] represents the influence of occurrence states on the pore space. These three mixed occurrence states can be modeled using a unified workflow, in which the volume fractions of various gas hydrate types are expressed in general forms in terms of the apparent porosity. In addition, the model considers the effect of a pore filler on shear modulus. The developed model is validated through calibration with real well-log data and published experimental data corresponding to five gas hydrate-bearing formations. The model effectively interprets the influences of gas hydrate saturation and occurrence state on these formations. Thus, the generalized model provides a theoretical basis for the analysis of sensitive elastic parameters and quantitative interpretation for gas hydrate reservoirs.

Rock-physics-guided machine learning for shear sonic log prediction

The S-wave velocity ([Formula: see text]) is a vital parameter for various petrophysical, geophysical, and geomechanical applications in subsurface characterization. Nevertheless, obtaining shear sonic log is frequently challenging because of its high economic, time, and operating costs. Conventional methods for predicting [Formula: see text] rely on empirical relationships and rock-physics models, which often fall short in accuracy due to their inability to account for the complex factors influencing the relationship between [Formula: see text] and other parameters. We develop a physics-guided machine learning (ML) approach to predict the shear sonic log using various physical parameters (e.g., natural gamma ray, P-wave velocity, density, and resistivity) that can be readily obtained from standard logging suites. Three types of rock-physical constraints combined with three guidance strategies form the various physics-guided models. Specifically, the three constraint models include mudrock line, empirical P- and S-wave velocity relationship, and multiparameter regression from the logging data, and the three guidance strategies involve physics-guided pseudolabels, physics-guided loss function, and transfer learning. To assess the model’s generalization ability and simulate the lack of labeled data in real-world applications, a single well is used as a training well, whereas the remaining four wells are used to blind test in a clastic reservoir. Compared with supervised ML without any constraints, all models incorporating physical constraints demonstrate a significant improvement in prediction accuracy and generalization performance. This underscores the importance of integrating the first-order physical laws into the network training for shear sonic log prediction. The most successful approach combines the multiparameter regression relationship with the physics-guided pseudolabels in this case, resulting in a remarkable 47% reduction in the average root-mean-square error during the blind test.