Аңдатпа
ABSTRACT: The Discrete Element Method (DEM) modeled by coarse-graining approaches has been gaining importance in many applications including a triaxial compression test that represents the key micromechanical behaviors of deformable solids. In this work, the triaxial compression test has been investigated using a realistic particle-size distribution sample that replicates the weak cemented sandstone in Kazakhstan. The simulation was performed by the modified Johnson-Kendall-Roberts (JKR)-based DEM model with cohesive contact forces. According to the model, bonds between particles are broken at the maximum value of the normal contact force, and no new bonds are formed after the breakage. In this research, we implemented the coarse-graining methods for the modified JKR model. A scaling law was developed for each material parameter in the model such as density, surface energy density, Young’s modulus, Poisson’s ratio, friction and restitution coefficients . In addition, those approaches were combined with the same statistic weight (SSW) and same size parcel (SSP) scaling methods in order to scale the polydisperse particles. These combined methods are based on the conservation of mass and momentum by applying statistic weights to the particle diameters. First, the original particle simulation with the modified JKR settings was validated with the experimental data. Then, the stress-strain response and bonding behavior were compared between the original particle system and coarse-grained simulations. There is a good agreement between the results obtained from the original particle system and coarse-grained particle models. Moreover, the speedup of the coarse-grained simulations in parallel computing is achieved approximately 15 times faster than the original particle system with the same number of cores. 1. INTRODUCTION Sand production negatively affects the operation in the oil and gas industry. It usually occurs with weak unconsolidated sandstone reservoirs where the stress on the formation exceeds the mechanical strength of the rock, which leads to its failure. The produced sand blocks the flow line, destroys the equipment by erosion, and prevents access to the wellbore. As a result, the operational cost increases due to production limitations. The prediction of sand production optimizes the best strategy for sand control management to minimize those limitations.