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In this paper, based on the background of a deep shale reservoir, a solid–liquid two-phase flow model suitable for proppant and fracturing fluid flow was established based on the Euler method, and a large-scale fracture model was established. Based on field parameters, a proppant transport experiment was conducted. Then, on the basis of the experimental fracture model, proppant transport simulation under different influencing factors was carried out. The results show that the laboratory experiment was in good agreement with the simulated results. The process of proppant accumulation in fractures can be divided into three stages according to the characteristics of sand banks. The displacement mainly affects the sedimentation distance of the proppant in the first stage, and the viscosity of the fracturing fluid represents the strength of the fluid sand carrying performance. Compared with 40/70 mesh proppant, 70/140 mesh proppant is more easily fluidizable, the fracture width has less influence on proppant migration and placement, and the perforation location only affects the accumulation pattern at the fracture entrance, but has less influence on proppant placement in the remote well zone. [ABSTRACT FROM AUTHOR]

The moth-flame algorithm shows some shortcomings in solving the complex problem of optimization, such as insufficient population diversity and unbalanced search ability. In this paper, an IMFO (Improved Moth-Flame Optimization) algorithm is proposed to be applied in solving the optimization problem of function. First, cat chaotic mapping is used to generate the initial position of moth to improve the population diversity. Second, cosine inertia weight is introduced to balance the global and local search abilities of the algorithm. Third, the memory information in the particle swarm algorithm is introduced into the iterative process of the algorithm to speed up the convergence of the population. Finally, Gaussian mutation strategy is used in the current optimal solution to avoid the algorithm from falling into the local optimum. Simulation experiments are conducted on 11 benchmark test functions, compared with other improved MFO (Moth-Flame Optimization) algorithms and classical optimization algorithms. The results show that the IMFO has higher accuracy and stability in solving the above-mentioned test functions. The proposed algorithm is experimented and verified by optimizing the KELM (Kernel Extreme Learning Machine) in an engineering example and exhibits a better optimization performance. [ABSTRACT FROM AUTHOR]