Bo Zhou

Understanding the mechanical behavior of gas hydrate-bearing sediments (GHBSs) is critical for safe hydrate extraction. This study employs the discrete element method (DEM) in PFC2D to investigate the macro- and meso-mechanical properties of GHBSs. A novel layered staining technique is proposed to enhance deformation visualization in membrane-bound GHBS samples. Biaxial simulations were conducted on samples with varying hydrate saturations. Results demonstrate that the deviatoric stress–axial strain curves exhibit strength enhancement with increasing hydrate saturation, while volumetric strain–axial strain curves reveal distinct contraction–dilation behavior. Shear band formation observed during failure highlights the shear-dominated damage mechanism. At the mesoscale, key parameters—coordination number, deviatoric fabric, contact force, and particle rotation—were systematically analyzed. The coordination number initially increases and subsequently decreases with axial strain, correlating with the contraction–dilation transition. Similarly, deviatoric fabric and contact force evolution align with the hardening–softening characteristics of GHBSs. Notably, intensified particle rotation within shear bands underscores its pivotal role in shear localization. These findings bridge mesoscopic mechanisms to macroscopic behavior, providing insights for optimizing hydrate extraction strategies. It should be noted that the simulations are two-dimensional plane–strain idealizations and adopt a single hydrate occurrence pattern; the quantitative results are therefore conditioned by these assumptions, and their extension to other occurrence patterns and to three dimensions is left for future work.

Geothermal wellbore stability in deep formations is governed by pronounced thermo–hydro–mechanical (THM) coupling, in which seepage-induced pore pressure diffusion plays a critical yet frequently underestimated role. Many existing thermoelastic models neglect seepage processes or treat pore pressure as a simplified boundary condition, thereby restricting their predictive capability in deep, high-temperature geothermal reservoirs. To overcome this limitation, this study develops a three-dimensional thermoelasticity model with explicitly coupled seepage effects to investigate stress redistribution around geothermal wells under complex anisotropic loading conditions. The proposed framework incorporates depth-dependent mechanical, hydraulic, and thermal parameters, enabling a more realistic characterization of deep formation behavior. The results demonstrate that seepage-driven pore pressure diffusion fundamentally reconfigures near-wellbore stress evolution, with stress perturbations intensifying with burial depth due to enhanced hydraulic–thermal gradients and anisotropic stress amplification. Overall, this study establishes a rigorous thermoelastic analytical framework with explicit seepage coupling, providing improved predictive capability for geothermal wellbore stability and highlighting the importance of integrating hydraulic and thermal regulation into deep geothermal drilling design.

This study develops a hydro-mechanical (HM) multi-field coupled model to track stress evolution in reactive materials like cementitious composites. These materials exhibit highly non-steady-state bulk and interfacial stresses during the transition from liquid to porous solid. Unlike conventional structural analyses, our model accounts for phase transition processes and dynamic boundary conditions. It specifically highlights how material strength development drives load transfer and how endogenous volumetric strain redistributes system stress. By incorporating nonlinear constitutive parameters correlated with the degree of reaction, the model achieves a precise description of the spatiotemporal mechanical response of the sheath system under annular confinement. Validation results demonstrate that the calculated values are in high agreement with experimental pressure evolution trends. The findings reveal that internal volumetric shrinkage is the core driver for the reduction of radial compressive stress and the generation of circumferential tensile stress. Furthermore, the interplay between initial hydrostatic pressure and the evolution of skeletal stiffness dictates the medium-to-long-term evolution path of interfacial stress. This research elucidates the mechanical pathways of stress redistribution in reactive materials during constrained phase transitions, providing a theoretical framework for the structural integrity assessment of heterogeneously evolving materials under complex constraints.

Understanding the mechanical behavior of gas hydrate-bearing sediments (GHBS) is critical for safe hydrate extraction. This study employs the discrete element method (DEM) in PFC2D to investigate the macro- and meso-mechanical properties of GHBS. A novel layered staining technique is proposed to enhance deformation visualization in membrane-bound GHBS samples. Biaxial simulations were conducted on samples with varying hydrate saturations. Results demonstrate that the deviatoric stress–axial strain curves exhibit strength enhancement with increasing hydrate saturation, while volumetric strain–axial strain curves reveal distinct contraction-dilation behavior. Shear band formation observed during failure highlights the shear-dominated damage mechanism. At the mesoscale, key parameters—coordination number, deviatoric fabric, contact force, and particle rotation—were systematically analyzed. The coordination number initially increases and subsequently decreases with axial strain, correlating with the contraction-dilation transition. Similarly, deviatoric fabric and contact force evolution align with the hardening-softening characteristics of GHBS. Notably, intensified particle rotation within shear bands underscores its pivotal role in shear localization. These findings bridge mesoscopic mechanisms to macroscopic behavior, providing insights for optimizing hydrate extraction strategies.

This paper proposes a novel machine learning method of finite element stress recovery, which is based on the feature variables of coordinates and displacements, to improve stress recovery accuracy. Four distinct machine learning algorithms—Backpropagation (BP) Neural Network, Radial Basis Function (RBF) Neural Network, Random Forest (RF), and Support Vector Regression (SVR)—are applied to both linear and nonlinear stress fields using linear and quadratic finite elements. Results indicate that the proposed method achieves higher accuracy in recovering the three node stress components compared to traditional methods, especially in small sample scenarios where its advantages are more evident. For linear stress fields with limited samples and a higher prevalence of outliers, the SVR algorithm demonstrated superior recovery performance, outperforming traditional finite element software. In contrast, for nonlinear stress fields with larger sample sizes and complex data relationships, the RBF Neural Network demonstrated the best recovery outcomes, also surpassing standard finite element software. Furthermore, quadratic elements consistently showed higher stress recovery accuracy than linear elements. These findings offer valuable insights and practical implications for enhancing stress recovery methods in finite element analysis.

A novel shear damage model based on homogenization theory and a modified Mohr-Coulomb criterion is proposed to predict the full deformation process of gas

Casing stress distribution is one of the most relevant topics in the study of the wellbore stress field. Due to factors such as uneven geostress, the wellbore is often elliptical, and the casing may not necessarily be in the center of the wellbore. This article adopts the theory of elasticity and uses the finite element method for calculation. The main research focuses on the effects of ovality and eccentricity on the overall stress distribution of the casing and the stress distribution at the interface between the casing and cement sheath (i.e., the outer wall of the casing). The research results indicated that when the casing is centered, higher ellipticity is beneficial for improving the casing’s response to non-uniform stress. However, when the casing is eccentric, higher ellipticity significantly increases the non-uniformity of the stress distribution and the risk of stress concentration in the casing. The risk of casing failure increases with increasing non-uniform stress. Additionally, a lower elastic modulus and higher Poisson ratio of the cement can improve the casing’s response to stress. This research result has a certain reference value for implementing high-quality cementing construction and ensuring the normal production of oil and gas wells.

This work focuses on the theoretical investigation of the thermo-mechanical behaviors of a geothermal well in the formation with anisotropic in-situ stress. First of all, the complex problem of a geothermal well is divided into three simple subproblems: the uniform load subproblem, the cosine load subproblem and the temperature load subproblem. The uniform load subproblem and the temperature load subproblem are analytically solved using the displacement method in elasticity theory, while the cosine load subproblem is analytically solved using the stress function method in elasticity theory. The analytical results of subproblems are compared with the numerical results from finite element method to verify the obtained analytical solutions of subproblems. Then, a thermo-mechanical analytical model of a geothermal well, which includes the analytical formulations of displacement field and stress field, is proposed by superposing the analytical solutions of the above three subproblems. At last, the proposed analytical model is used to investigate thermo-mechanical behaviors of a geothermal well in the formation with anisotropic in-situ stress. Some important influence factors on the stress field of cement sheath of the geothermal well are analyzed and discussed comprehensively. Some valuable results are obtained, which also show that the proposed analytical model can effectively predict the thermo-mechanical behaviors of a geothermal well in the formation with anisotropic in-situ stress. This work is useful for the research on mechanical integrity design of a geothermal well and other related research in oil-gas well engineering.

In this work, an analytical method based on machine learning (AM-BML) is proposed to predict the stress distribution around a cased borehole in the formation with anisotropic in-situ stresses. Firstly, the stress field equations with undetermined coefficients are derived using the elasticity theory to formulate the stress field near the cased borehole. Secondly, the regression functions of a machine learning algorithm, least squares support vector machine (LS-SVM), are constructed according to the derived stress field equations. Thirdly, the undetermined coefficient equations are developed to determine the undetermined coefficients in the derived stress field equations according to the constructed LS-SVM regression functions and the derived stress field equations. The derived stress field equations and the developed undetermined coefficient equations together constitute the proposed AM-BML, which can well predict the stress distribution around a cased borehole in the formation with anisotropic in-situ stresses. Compared with the traditional analytical methods, the proposed AM-BML is more convenient for practical applications because it is difficult and complex to determine the undetermined coefficient in the stress field equations according to the traditional analytical methods. Finally, the proposed AM-BML is validated through the comparisons with numerical simulation experiments; and it is also used to investigate the influencing factors on the stress field of a cased borehole system, which gives some useful results. This work is helpful for the study of borehole stability and the other study related to the petroleum engineering.