Multi-stage plug-n-perf fracturing is commonly used for horizontal well completion in unconventional reservoirs. Uniform distribution of proppant across all clusters in each stage has proven to be challenging with low viscosity slickwater due to its limited transport capability. Computational Fluid Dynamics (CFD) has been used to model proppant transport in wellbore to improve perforation and fracturing design for achieving uniform proppant placement. However, traditional CFD modeling of a full-scale stage is computationally expensive, which limits its applicability in the completion design optimization. A new approach was developed in this paper to efficiently predict proppant placement along a multi-cluster stage based on a machine learning (ML) model trained with extensive CFD modeling results. Its high computational efficiency permits quick sensitivity analyses to optimize perforation and fracturing designs. The new approach was validated against full-stage CFD modeling results as well as post-treatment field diagnostics. Sensitivity analyses show that the inertia effect is key in heel clusters with a high slurry flowrate, which tends to push proppant to the toe due to its high density. Proppant settling allows bottom perforations to accept more proppant than top perforations. This gravitational effect is not negligible near the heel at high flowrates, regardless of the turbulent dispersion, and becomes more dominant near toe clusters where the flowrate is reduced. As a result, a near-uniform proppant placement is achievable via perforation design optimization by taking advantage of these two key mechanisms controlling proppant transport in horizontal wellbores. It is demonstrated that in-line perforating designs with all perforations having the same orientation in each cluster or the entire stage, especially with perforations at the bottom or on the side of the wellbore, improve the proppant placement uniformity. However, it is recommended that the optimum perforation design should be identified case by case depending on specific input parameters. The ML-based model developed in this study has overcome the limitations suffered by the existing models in the literature and is able to provide quick and yet reliable solutions to proppant placement prediction and design optimization.

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