Abstract:

Proppant transport and placement within hydraulic fractures directly determine effective fracture conductivity, stimulated reservoir volume utilization, and long-term post-fracturing productivity, making them critical factors governing the success of hydraulic fracturing in unconventional reservoirs. During hydraulic fracturing, proppant migration is a complex solid-liquid two-phase transport process involving particle settling, suspension, deposition, re-transport, and redistribution, which is jointly controlled by fluid properties, particle characteristics, fracture geometry, and operating conditions. In recent years, research on proppant transport has progressively evolved from investigations of particle settling and proppant bank development in idealized single fractures toward the characterization of heterogeneous placement behavior, multiscale simulation, and rapid prediction under complex fracture networks, rough-walled fractures, and dynamically propagating fracture conditions. Following the framework of experimental observation-mechanistic understanding-numerical simulation-intelligent prediction, this paper systematically reviews recent advances in experimental methods, key physical mechanisms, numerical simulation approaches, and data-driven prediction techniques for proppant transport and placement in hydraulic fractures. Experimental studies have established fundamental understandings of particle settling, proppant bed formation, equilibrium height evolution, and transport front migration in smooth single fractures. With increasing attention to realistic reservoir conditions, experimental systems have been extended to complex fracture networks, rough fracture surfaces, and reconstructed fractures with dynamic loading conditions, revealing the significant influence of fracture junction flow partitioning, rough-wall effects, heterogeneous aperture distribution, and local geometric constraints on proppant placement. Numerical simulation methods have further improved the understanding of proppant transport mechanisms beyond the limitations of experimental observations. Existing models can generally be classified into Eulerian-Eulerian and Eulerian-Lagrangian approaches. Eulerian-Eulerian methods, including mixture and two-fluid models, provide advantages in computational efficiency and engineering-scale prediction, whereas Eulerian-Lagrangian methods, including CFD-DEM, DDPM, and MP-PIC, retain particle-scale information and enable more detailed analysis of particle interactions, non-uniform placement, and local bridging or plugging mechanisms. However, achieving a balance between physical accuracy, computational efficiency, and engineering applicability remains a major challenge, particularly for dynamic fracture propagation, rough fracture surfaces, non-spherical particles, and multiscale fracture systems. In addition, intelligent prediction methods based on mechanism-data fusion have recently emerged as promising approaches for accelerating high-fidelity simulations and improving real-time decision-making. Surrogate models, physics-informed neural networks, and data assimilation techniques provide new possibilities for rapid prediction, parameter optimization, and dynamic model updating. Nevertheless, these approaches still face limitations related to data availability, physical consistency, and generalization capability under complex field conditions, and they cannot yet replace physics-based models. Current challenges mainly include insufficient characterization of realistic fracture geometries, incomplete coupling among dynamic fracture propagation, proppant transport, and fluid leakoff processes, limited representation of rough-wall effects and non-spherical particle behavior, and the lack of reliable field-scale validation data. Future research should integrate high-fidelity experiments, advanced numerical simulations, physics-constrained surrogate models, and field data assimilation to establish a unified multiscale and multiphysics framework, thereby improving the prediction capability of proppant placement and supporting hydraulic fracturing design optimization and real-time operational decision-making.

Key words: hydraulic fracturing, proppant transport, complex fracture networks, non-uniform proppant placement, mechanism-data fusion

摘要：

水力裂缝内支撑剂的运移与铺置直接影响裂缝导流能力、有效改造体积及压后长期产能，是决定非常规储层压裂效果的重要因素。支撑剂运移属于典型的固液两相输运过程，其行为受到颗粒沉降、流体携带、裂缝形态、壁面特征及施工参数等多因素共同控制。近年来，相关研究已由理想单裂缝条件下的颗粒沉降与砂堤演化，逐步拓展至复杂裂缝网络、粗糙裂缝及动态扩展裂缝条件下的非均匀铺置、多尺度模拟和快速预测。本文围绕“实验观测—机理认识—数值模拟—智能预测”这一主线，系统综述了水力裂缝内支撑剂运移与铺置研究进展。实验研究方面，光滑单裂缝实验揭示了颗粒沉降、砂堤形成及运移前缘演化等基本规律；复杂缝网、粗糙裂缝及真实裂缝重构实验进一步揭示了裂缝节点分流、壁面粗糙度、非均匀开度及局部几何约束对支撑剂空间分布的影响。数值模拟方面，现有方法主要包括欧拉—欧拉和欧拉—拉格朗日两类。前者具有计算效率高、适用于工程尺度预测等优势，后者通过保留颗粒离散特征，能够更精细地描述颗粒相互作用、非均匀铺置以及局部架桥堵塞等微观机制。不同模型在物理真实性、计算效率和工程适用性之间存在明显差异，需要根据研究尺度和目标进行合理选择。近年来，基于机理—数据融合的智能预测方法为支撑剂运移研究提供了新的技术路径。代理模型、物理信息神经网络及数据同化方法能够实现高保真模型加速、参数优化和状态更新，为复杂裂缝条件下的快速预测与实时决策提供潜力。然而，智能方法目前仍受到数据规模、物理一致性和复杂工况泛化能力等因素限制，尚不能替代高保真机理模型。当前研究仍面临真实裂缝结构表征不足、动态裂缝扩展—支撑剂运移—流体滤失全过程耦合不充分、粗糙壁面及非球形颗粒机制描述有限以及现场验证数据缺乏等问题。未来应结合高真实性实验、高精度数值模拟、物理约束智能模型及现场数据同化技术，构建多尺度、多物理场协同的研究框架，为支撑剂铺置预测、压裂设计优化及现场实时决策提供理论支撑。

关键词: 水力压裂, 支撑剂运移, 复杂裂缝网络, 非均匀铺置, 机理—数据融合

CLC Number: 

• TE357

• O359