Numerical simulation of mechanical compaction and pore evolution of sandstone considering particle breakage

Abstract

Mechanical compaction is an important diagenetic process in sandstone reservoirs. Particle breakage, which commonly occurs during mechanical compaction, plays a significant role in controlling the physical properties of the reservoir. However, existing numerical simulation methods have limitations in simulating mechanical compaction when considering particle breakage. In this study, a discrete element simulation method of mechanical compaction was proposed based on particle cutting, and the experimental results reported in the literature were used to calibrate the simulation parameters. Finally, this method was applied to the simulation of the mechanical compaction of sandstone to analyze the pore evolution process. The results show that the new simulation method has high computational efficiency and can reflect the process of particle breakage. The simulation results coincide well with the experimental results. In the simulated mechanical compacted process of coarse sandstone, particle breakage is strong in the high-stress stage with a vertical stress of 30 MPa–50 MPa. The porosity and mean radii of pores and throats decreased rapidly, and the number of pores and throats increased rapidly in the high-stress stage. When the vertical stress reached 50 MPa, compared to the simulation results without considering particle breakage, the porosity difference rate caused by particle breakage was 4.63%; the radius difference rates of pores and throats were 2.78% and 6.8%, and the number difference rates of pores and throats were 4.95% and 8.74%, respectively. In the process of mechanical compaction, the pore evolution of the reservoir is controlled by the filling of the pre-existing pore space by the fragments generated through particle breakage and the generation of microfractures. Additionally, the simulation method presented in this study can be applied to complex geological conditions and can be combined with other reservoir simulation methods. The simulation results can provide rich training samples for artificial intelligence and other emerging technologies.