发表机构
Lawrence Livermore National Laboratory; Northwestern University; EarthFlow AI（劳伦斯利弗莫尔国家实验室; 西北大学; EarthFlow AI）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出基于孔隙力学的化学-力学耦合框架，整合入反应运移与岩土力学模拟，可准确模拟多孔岩石中矿物沉淀溶解的耦合过程，为工程地下系统油藏尺度分析提供物理预测方法。
AI 中文摘要
岩石中矿物的沉淀与溶解会改变孔隙结构、应力及水力特性，形成紧密耦合的化学-流体-力学过程，这是地下系统的核心。然而，现有连续尺度模拟器缺乏通用、数学上可处理且物理上严谨的化学-力学公式来模拟这些耦合过程。为解决这一空白，本研究提出一种基于孔隙力学的化学-力学耦合框架，并将其整合到耦合的反应运移与岩土力学模拟中。该框架以经典孔隙力学理论为基础，对宿主岩石、孔隙流体及孔隙矿物的应力状态给出不同描述，能在矿物沉淀与溶解过程中严格且灵活地处理三者各自的行为与力学相互作用。数值算例表明，该基于孔隙力学的方法通过将孔隙尺度的矿物生长转化为矿化压力，可捕捉预期的力学响应；通过考虑矿物压缩性，还能支持更符合物理实际的变形与诱导应力表征。与本征应变法的对比进一步凸显，该基于孔隙力学的方法无需明确呈现孔隙几何或其他微观结构特征，即可模拟有效应力演化与裂缝发育的独特能力。总体而言，该框架为模拟反应性多孔岩石中的耦合化学-力学过程提供了一种通用、基于物理的预测方法，可支持未来对工程地下系统的油藏尺度分析。
英文摘要
Mineral precipitation and dissolution in rocks alter pore structure, stress, and hydraulic properties, producing tightly coupled chemo-hydro-mechanical processes that are central to subsurface systems. Yet, existing continuum-scale simulators lack a generalizable, mathematically tractable, and physically grounded chemo-mechanics formulation for modeling these coupled processes. To address this gap, this work presents a poromechanics-based framework for chemo-mechanics coupling and integrates it into coupled reactive transport and geomechanics simulations. Building upon classical poromechanics theory, the framework provides distinct descriptions for the stress states of the host rock and pore minerals, in addition to pore fluid pressure, enabling a rigorous and flexible treatment of their individual behavior and mechanical interactions during precipitation and dissolution. As demonstrated by numerical examples, the poromechanics-based method captures the expected mechanical response by translating pore-scale mineral growth into mineralization pressure. By accounting for mineral compressibility, the method also supports more physically realistic representations of deformation and induced stress. Numerical comparisons further highlight the unique capability of the poromechanics-based approach to model effective stress evolution and fracture development without explicitly representing pore geometry or other microstructural features. Overall, this framework offers a versatile, physics-based predictive approach to modeling coupled chemo-mechanical processes in reactive porous rocks and supports future reservoir-scale analyses of engineered subsurface systems.
