Rock Parameters Research Articles

The Particle Flow Code (PFC) is widely applied in rock engineering, discretizing materials like rock and soil into assemblages of abstract particle elements. By assigning microparameters to particle contacts and applying Newton’s laws of motion along with force-displacement laws at contacts, PFC effectively replicates the macroscopic mechanical behavior of rock materials. However, the calibration process of microparameters in particle flow models is complex, time-consuming, and often lacks clear guidance. Therefore, there is an urgent need for a novel method to establish a relationship between the macroscopic mechanical parameters of rock and its micromechanical parameters. Based on laboratory uniaxial compression tests and utilizing the PFC2D particle flow software, this study investigates the relationship between the microparameters and macroscopic mechanical parameters of shale. A microparameter calibration method using the stacking ensemble learning algorithm is proposed to calibrate the microparameters in the shale particle flow model. Numerical simulation results demonstrate that the calibrated macroscopic mechanical parameters of the shale achieved over 90% accuracy, with a low residual mean square error, indicating strong stability and reliability in the predictive model. Additionally, uniaxial compression numerical simulation tests were conducted to validate the model’s accuracy, showing high consistency between the simulated and laboratory test results in terms of macroscopic mechanical parameters, stress-strain curves, and failure modes, thereby verifying the effectiveness and accuracy of the stacking ensemble learning algorithm in calibrating the microparameters of shale.

ABSTRACTRock tunnel engineering (RTE) plays a crucial role in modern infrastructure development. The development of artificial intelligence (AI) is able to drive transformative advances in RTE. This review provides an in‐depth analysis of the AI application in RTE. Through a comprehensive examination of existing literature, we explore how AI technologies have revolutionised various aspects of RTE, including construction methodology, rock parameter estimation, hazard disaster management during construction, and tunnel operation. In addition, we provide an in‐depth study of the synergies between various AI algorithms and related open datasets. This work also outlines promising future research directions for the AI application in RTE, aiming to inspire further advancements in this emerging field. In conclusion, this review underscores the positive influence of AI on RTE, emphasising its capacity to elevate efficiency, accuracy, and safety standards throughout various phases of tunnel projects. The convergence of AI with RTE holds immense promise for advancing the field and ensuring the success and sustainability of future tunnel infrastructure endeavours.

In the Chengdao–Zhuanghai area, there are few core samples of Mesozoic clastic rocks but abundant logging data. It is difficult to establish a fracture model of clastic rocks directly based on core samples and relevant tests. In this study, triaxial compression tests are conducted on Mesozoic clastic rock samples to reveal the failure mechanism of clastic rocks. A statistical model based on logging data is utilized to calculate dynamic rock mechanical parameters, and theoretical relationships between static and dynamic mechanical parameters are derived. A failure model for clastic rocks is established using logging data and the minimum energy consumption principle by applying the principle of minimum energy consumption and adopting the unified energy yield criterion of rocks as the energy consumption constraint. This research study shows that a linear relationship exists between the static and dynamic mechanical parameters of Mesozoic clastic rocks, and the correlation coefficient can reach 85%. The core aspect of clastic rock failure is energy dissipation. As confining pressure increases, more energy must be dissipated during the failure of clastic rocks. Upon failure, the releasable elastic energy accumulated within the clastic rocks clearly reflects the confining pressure effect. A higher initial confining pressure leads to a greater release of elastic energy and results in a more severe failure degree. The developed rock failure model effectively represents the nonlinear mechanical behavior of Mesozoic clastic rocks in the Chengdao–Zhuanghai area under complex stress conditions. It is suitable for investigating the fracture distribution of Mesozoic clastic rocks and addresses the challenge of understanding the failure mechanism of these rocks in the Chengdao–Zhuanghai region.

In this study, the Tuanjie Tunnel project on the Tongwei-Dingxi Expressway is utilized to investigate the stress-seepage coupling in loess tunnels. Field monitoring, laboratory experiments, and numerical simulations were employed to establish a coupled numerical model of the stress-seepage field for the shallow-buried sections of these tunnels. The seepage-stress interactions in loess tunnels were analyzed, revealing variations in pore water pressure around the tunnel and the deformation behavior of surrounding rock during construction, with particular attention to the effects of water migration.The results indicate that when the groundwater level is 10 m from the tunnel crown, the pressure of pore water at various measurement points follows an order of tunnel invert > arch springing > arch waist > arch haunch > tunnel crown. Within the pipe roof reinforcement zone, pore water pressure increases with distance from the tunnel perimeter, while above the zone, it decreases with distance. When considering water migration, the excavation of the upper bench significantly impacts the vertical effective stress at each point, the excavation of the middle bench impacts the arch wall and the haunch, and the excavation of the lower bench impacts the springing of the arch.Based on these insights, addressing the challenges encountered during the construction of water-rich loess tunnels, the implementation of pipe roof reinforcement measures for surrounding rock has played a positive role in enhancing the stability of loess tunnels during construction.

In mining operations, the rock mass located between the water-conducting fault fracture zone and the waterproof protective coal column is highly susceptible to damage, which may result in sudden water inrush disasters. This paper first employs indoor experiments and on-site rock sample analysis to determine the macroscopic mechanical parameters of rocks and rock masses, as well as the microscopic mechanical parameters of block contacts. The fracture and seepage evolution mechanisms in the mining-induced rock mass adjacent to major faults were analyzed utilizing the discrete element-fluid coupling theory in Universal Distinct Element Code (UDEC). The results identified three primary pathways for water hazards caused by mining: the calculated stress field and seepage field indicated that the formation of the water-inrush channels was determined by the parameters of coal seam mining. Different waterproof protective coal columns were set up for the three geological conditions under study. Additionally, a “claw-shaped” detection and flow monitoring method has been proposed for small water-conducting faults. These findings are important and provide valuable guidance for understanding and managing water inrush hazards in mining operations near major faults.

Reservoir porosity is a crucial indicator of the physical properties of reservoirs, forming the foundation for oil and gas exploration, development design, and decision-making. Currently, it is primarily obtained through core testing or logging interpretation, but the lack of quantitative evaluation methods during drilling limits the timeliness and efficiency of porosity acquisition. Based on this, this study focuses on the tight sandstone reservoir in the East China Sea shelf basin, conducting modeling and rock-breaking simulations of 5 blade and 6 blade polycrystalline diamond compact (PDC) bits commonly used in the region. It investigates the relationships between rate of penetration (ROP), torque, mechanical specific energy (MSE), physical index, and other parameters for rocks with varying physical characteristics. A real-time quantitative prediction method for reservoir porosity, based on drilling and logging engineering parameters, is proposed. The results indicate that: (1) Significant differences in the response characteristics of rate of penetration, torque, and MSE are observed when drilling formations with identical mechanical characteristics, due to the influence of bit type. Therefore, these engineering parameters are not suitable for directly predicting reservoir porosity. (2) The relationship between the physical index and elastic modulus for 5 blade and 6 blade PDC bits is highly consistent, with both increasing logarithmically as elastic modulus increases. This suggests that the physical index can eliminate the influence of bit type and more accurately reflect changes in formation characteristics during drilling. (3) Using elastic modulus as an intermediary parameter, a model is established that relates porosity to the physical index, showing that porosity decreases as a power function of the physical index. The research findings were cross-verified in well NB13-4-A, with a 91.57% agreement between the porosity predicted by engineering parameters and the logging-derived porosity. The prediction method was applied to 20 exploration wells in the NB13-4 working area, yielding an average porosity consistency rate of 85.74%. This demonstrates that the method can provide timely, efficient, and accurate support for decision-making in exploration operations, such as intermediate testing and well completion.

Abstract In geological exploration and tunnel/underground engineering, precise, rapid, and intelligent rock lithology identification is crucial. A wavelet scattering transform-support vector machine (WST-SVM) rock image classification method is proposed that combines WST with SVM to address the limitations of conventional convolutional neural networks reliant on annotated samples. The method extracts multi-scale features from rock images using WST and trains an SVM classifier, achieving superior performance in test accuracy, macro-average precision, recall, and F1-score on a dataset of six rock types. Parameter analysis reveals that increasing invariant scale, decomposition transformations, and quality factor enhances feature matrix dimensionality and computational time. This approach reduces the need for extensive annotated samples and provides a practical solution for improving the accuracy and efficiency of rock lithology identification in geological exploration and tunnel engineering.

Using an extended data bank of the Ukrainian Shield, a search for correlations between petrophysical parameters of rocks was conducted. The differentiation of selected groups of rocks of different mineral composition by the propagation velocities of elastic waves and density at different pressures and temperatures has been confirmed. Unambiguous, mainly directly proportional dependencies have been established between elastic, elastic and electrical parameters with high enough pair-wise correlation coefficients.

Reservoir geomechanical modeling is considered one of the important parts of the research accomplished on planning oil and gas fields’ exploitation and drilling. The present research aimed at investigating the changes of geomechanical elastic coefficients and in-situ stress field before and after water flooding in carbonate reservoirs of Asmari reservoir of Ahvaz oil field in southwest Iran. The results reveal changes in geomechanical parameters before and after the water flooding process. Specifically, Young’s modulus decreased from 21 to 18 GPa, while it increased from 14.22 to 23.77 GPa due to the flooding process and an increase in temperature from 25 to 75 °C. Likewise, the friction coefficient has increased from 32.21 (before the water flooding process) to 38.53 (after the mentioned process). Similarly, the bulk modulus before the flooding process decreased from 12.23 to 10.27 GPa after the flooding process. In addition, increasing the temperature from 25 to 75 °C after the flooding process caused an increase in the bulk coefficient from 8.4 to 12.7 GPa.Similarly, the changes in the shear modulus before the flooding process decreased from 8.67 to 7.7 GPa after flooding. Moreover, an increase in temperature from 25 to 75 °C caused an increase in shear modulus from 5.8 to 9.9 GPa. Furthermore, examining the changes in Mohr’s circles before and after the flooding process by water fluid showed that these circles will change.

Rock failure mechanisms based on rheological dynamics

Abstract As one of the important electrical parameters in the study of the earth’s medium, the complex resistivity of rock can help determine the temperature distribution, oil and gas reservoirs, mineral distribution, and underground water sources in the deeper parts of the earth. Therefore, specific reservoir parameters of rocks can also be indirectly obtained through simulation calculations and experimental analysis. This study combined classic induced polarization models such as Cole Cole model, Dias model, and Debye model to forward calculate the complex resistivity values of rocks. The complex resistivity values of different rock types calculated by the models were compared through rock physics experiments. Through a comparison with the experimental data and an analysis of the simulated induced polarization effect, the validity of the forward modeling results for various models was validated. Additionally, the relationship between the theoretical and experimental complex resistivity data of different rock types was examined. The research results have positive theoretical significance for helping with rock physics modeling, guiding the exploration and development of unconventional oil and gas resources in China, improving the accuracy of theoretical model inversion calculations and data interpretation, especially serving future oil and gas exploration and development work.

ABSTRACT An extensive understanding of the mechanical characteristics of rocks is essential for addressing wellbore-related problems, including wellbore instability, the inflow of sand, and reservoir collapse. Thus, to perform any activity, it is necessary to have continual profiles of the rock’s mechanical characteristics. Obtaining rock specimens in the reservoir at various depths and conducting lab testing is highly costly and time-consuming. These rock characteristics can thus be determined using the shear wave velocity measured from welllogging. However, this sonic log is frequently unrecorded due to several challenges, such as time and cost savings. For this reason, a field study was implemented in southern Iraq to address the objectives of this research. Simple and reliable mathematical models, i.e., multiple regression analysis (MRA) and artificial neural network (ANN), were developed for the clastic (shale and sandstone) formations to predict the shear wave velocity using well logging data, thence the profiles of the elastic rock parameters can be determined. The findings indicate that MRA and ANN are highly compatible with well-log data in accurately forecasting the shear wave velocity of clastic layers. ANN demonstrates superior accuracy compared to MRA, as evidenced by a better coefficient of determination (R2) of 0.92 and a lower mean squared error (MSE) of 0.06 for the shale formation, while 0.87 and 0.12, respectively, for the sandstone formation. Finally, this research proposed precise and costeffective techniques that can be applied to synthetic share wave velocity for clastic reservoirs, assisting geomechanical experts in constructing an accurate mechanical earth model.

The COMSOL Multiphysics multi-physical field coupling software is used to explore the seepage of coal rock in the true triaxial stress environment. The simulated working condition is the coal rock with a buried depth of more than 1500 m in ChengZhuang, JinCheng. The basic physical parameters of coal rock, such as elastic modulus, Poisson 's ratio, porosity and density, are obtained by uniaxial compression test and mercury injection test. The parameters are input into numerical simulation software to ensure the real reliability of simulation. At the same time, Darcy 's law is selected to define the seepage field, and the physical field of solid mechanics is added to ensure the construction of true triaxial stress environment. The temperature environment of coal seam is simulated by porous media heat transfer module. The simulation results are consistent with the experimental results.

The West Delta Deep Marine concession offshore Egypt’s Simian field was exposed to simultaneous pre-stack inversion to test its quantitative interpretation potential. In heterogeneous submarine channel reservoirs, characterising reservoir lithology and fluid distribution and isolating gas sand, brine sand, and background shale are the key Simian field challenges. Due to poor water sand mapping, numerous Simian field wells had surprising early water production rates. Therefore, this study may investigate if pre-stack seismic data and sophisticated inversion techniques can precisely pinpoint Simian field lithology, facies changes, and fluid distribution. Concurrent pre-stack inversion estimates rock parameters, including acoustic or P-wave impedance (Zp), shear impedance or S-wave impedance (Zs), and density (ρ), which are strongly related to lithology. Before the inversion procedure, two wells were analysed in a rock physics investigation, and three angle gathers (0–15°, 15–30°, and 30–45°) were pre-stacked inverted for Zp, Zs, P-wave velocity (Vp), S-wave velocity (Vs), Vp/Vs ratio, and ρ. Lambda-Mu-Rho (LMR) analysis involves obtaining Lamé parameters by inverting Zp and Zs simultaneously, resulting in Lamda-Rho (Incompressibility) (λρ) and Mu-Rho (Rigidity) (µρ) volumes. The training process involved pre-stack inversion analysis employing angle stack seismic data and well log data, cross-validation, and Vp, Vs, and Vp/Vs volumes. Vp/Vs and Zp volumes predicted Sw values that matched well gas-water contact. The study used a test well (Simian-Di) and validated the result at a blind well location (Simian-Dj) to evaluate a pre-stack inversion approach. It found accurate predictions, suggesting better output and economic efficiency.

The research presented in the article was undertaken in order to better investigate the generation potential of the Oligocene Menilite Formation due to its importance as source rocks within the Outer Carpathian Basin. The non-isothermal decomposition of the selected Carpathian source rock was studied to determine the kinetic parameters of the pyrolysis process. The kinetic parameters of bulk rock and separated kerogen were determined using the model-free Kissinger, Kissinger–Akahira–Sunose (KAS), and Friedman methods. The pyrolysis process exhibits a complex reaction mechanism. The obtained apparent activation energy (Ea) and pre-exponential factor (A) values depend on the extent of conversion, suggesting that the process involves multiple reaction steps. This dependence is very similar when calculated using both isoconversional methods, Friedman and KAS; however, the calculated values of the kinetic parameters differ depending on the method used. It was found that the activation energy of kerogen is lower than that of bulk rock, and the reaction maximum was shifted to higher temperatures. This shift is attributed to the presence of clay minerals in the rock. The values of average activation energy and the pre-exponential factor found in this study are relatively high, possibly due to the nature of the short-chain organic matter contained in the source rock.

To acquire Johnson–Cook (J-C) constitutive parameters that accurately depict the mechanical behavior of powder liner under conditions of high pressure, elevated temperature, and large deformation, as well as Holmquist–Johnson–Cook (HJC) constitutive parameters that precisely describe the dynamic damage of hard rock and make them suitable for numerical simulations for hard rock perforation, the present study introduces a constitutive parameter inversion method based on finite element simulation. Firstly, based on the experiments of perforating steel targets and underground perforating hard rock targets, a dynamic simulation of the perforating process of a shaped charge perforating target was carried out using ANSYS/LS-DYNA, and the influence law of each constitutive parameter on perforating depth and perforating aperture was systematically analyzed. Subsequently, the key parameters of the J-C constitutive model for powder liner and the HJC constitutive model for hard rock were optimized and determined using a response surface method, multi-genetic algorithm, and experimental data. A numerical simulation of the perforating process was finally conducted using the retrieved constitutive parameters of powder liner and hard rock, which were then compared with the experimental results. The results demonstrated that the discrepancy between the experimental and simulated data was within 5%, indicating that the constitutive parameters obtained through this inversion method could more reliably reflect the mechanical behavior of the powder mold and hard rock used in this study during perforation.

Gas chimneys can often be observed in marine seismic profiles, which are usually related to gas accumulation in shallow layers and their impacts on rock elasticity. To clarify the impact of various geologic factors on elastic wave propagation, an experimental study is conducted to elucidate the variation in rock elastic parameters in gas-accumulated rocks. Gas accumulation simulations and real-time ultrasonic monitoring experiments are conducted in laboratory experiments. The control factors for gas chimney formation, such as pressure, porosity, fractures, and fluids, are analyzed. It is found that high pore pressure provides favorable conditions for the formation of gas chimneys. Within the lower range of pore pressures, the P-wave velocity is more sensitive to gas changes, whereas the S-wave velocity is relatively insensitive. When the pore pressure is within a moderate range, the S-wave velocity is more sensitive than the P-wave velocity. At higher pore pressures, the influence of pore pressure on P-wave velocity is greater, and the influence on S-wave velocity is smaller. The experimental results for low-porosity sandstone are significantly influenced by pore pressure, whereas the attenuation of P and S waves in high-porosity sandstone is controlled by porosity and less sensitive to changes in pore pressure. The presence of fractures has a significant impact on velocity and amplitude, and the impact of fractures is no longer significant when the pore pressure is high. Injecting a small amount of gas into water-saturated rocks can cause a strong attenuation of wave amplitude, indicating that the influence of mixed fluids on amplitude is more significant than other factors. This study provides a theoretical explanation for gas chimney formations in shallow loose rocks and helps analysis of seismic wave propagation properties in the field.

IntroductionDue to the significant increase in plasticity under conditions of high temperature and pressure, the existing single brittleness evaluation methods prove inadequate for accurately characterizing the compressibility of deep shale in northeastern Sichuan, thereby severely limiting the optimal target selection and engineering modification in this region.MethodsThe focus of this paper is the deep Jurassic shale in northeastern Sichuan, studied through triaxial high-temperature and high-pressure tests, tensile tests, and X-ray diffraction experiments, which examine the mechanical properties of shale and the factors influencing them. The morphological characteristics of rock fractures under various loading conditions are analyzed, providing a standard for assessing brittleness factors and conducting a comprehensive quantitative evaluation.ResultsThe research concludes that the deep lacustrine shale exhibits traits of high elastic modulus and high Poisson’s ratio, with its brittleness largely influenced by mineral composition, the development characteristics of lamination, the degree of lamination development, and the anisotropy of the rock. Crack patterns have been analyzed to investigate the morphology of rock fractures. Through a correlation analysis of normalized rock parameters and the brittleness index derived from stress-strain curves with the fracture breakdown pressure and extension pressure observed in field fracturing, a comprehensive evaluation index has been established using the analytic hierarchy process to reflect the brittleness of deep lacustrine shale.DiscussionThis index serves effectively in characterizing the brittleness features of deep lacustrine shale, and evaluations suggest that the Liang upper section has a relatively high brittleness index and good compressibility, marking it as a key target layer for future shale gas development.

The lithology of the transitional facies of the Longtan Formation in the southern Sichuan Basin is complex, with soft/hard thin interlayers of mud shale, sandstone, and limestone. Drilling this layer often results in wellbore instability, including frequent blockages, tripping resistance, and sticking. This study focuses on a shale gas block in the Longtan Formation in Zigong, where a geomechanical profile was established by integrating ground stress, rock parameter tests, and logging data. The critical collapse pressure was calculated, and wellbore instability was simulated using the Mohr–Coulomb failure criterion and the discrete element method. Results indicate significant variability in the mechanical strength of the rocks, with notable longitudinal heterogeneity and a high risk of wellbore instability. The critical collapse pressure equivalent density ranges from 1.05–1.69 g/cm3. Under low-density conditions, wellbore expansion and reduction coexist due to local shear and dropping. Even when the drilling fluid density exceeds the collapse pressure equivalent, stress imbalance can still cause localized dropping at lithologic interfaces. These findings offer valuable insights into the mechanical mechanisms behind wellbore instability in formations with soft/hard thin interlayers and provide guidance for the prevention and control of wellbore instability and associated risks.

Numerous measurements show that after a strong earthquake the velocity of seismic waves changes and then slowly returns to its original value. The relaxation process can last from several months to several years, while the seismic wave velocity often changes logarithmically with time similar to what was observed in laboratory experiments. In this work, the relevant experimental results, including long-term observations at the Parkfield Seismic Observatory (California), are analyzed using the previously developed physical model that was successfully used to describe "slow time" effects in laboratory experiments. The model is based on an Arrhenius-type equation describing the evolution of contacts between grains and in cracks. The results of simulations of the recovery time are in satisfactory agreement with the experimental data. These results may be used for remote diagnostics of rocks' stress state.