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Summary Uniform proppant distribution in a cluster and a stage with multiple clusters is a primary objective to optimize fracturing parameters and improve the production from each cluster. Because fracturing slurry is typically pumped at high pressure and rate in fields, it is a big challenge to study proppant transport behavior and distribution characteristics through laboratory experiments. There is still a lack of an effective model to quantitatively evaluate proppant distribution based on an actual wellbore configuration. The objective is to propose a novel method to accurately evaluate the distribution uniformity and quickly optimize fracturing parameters based on field conditions. This paper conducts particle transport experiments in a horizontal pipe with six holes at the helical distribution. A 3D numerical model coupling of the computational fluid dynamics (CFD) and discrete element method (DEM) is used to study proppant distribution. Proppant distribution is quantitatively evaluated by the proppant transport efficiency (E) and normalized standard deviation (NSD). The effects of 10 parameters are investigated. An artificial neural network (ANN) model is developed to predict proppant distribution in a cluster. The results identify that proppant distribution among perforations is generally toe-biased in a horizontal wellbore due to a high pumping rate. Proppants with large inertia easily miss the heel-side holes and are suspended to the toe side. The complex vorticity flow carries them to the toe-side perforation regardless of hole orientation. Fluid distribution can significantly change proppant distribution regardless of fluid velocity. The heel-biased fluid distribution leads to the same bias of proppant, and the downward perforations receive more proppants. Proppant transport reaches equilibrium quickly, and the distribution is hard to change unless the injection condition varies. It is a good choice to increase fluid viscosity, add perforation sealers, and inject small mesh proppant, especially for the low density. The ANN model trained by extensive experimental and numerical samples can accurately evaluate proppant distribution uniformity. The study provides an efficient way to optimize injection parameter design and achieve real-time optimization coupled with the fiber-optic downhole diagnostic. It can be a crucial part of artificial intelligence hydraulic fracturing.

Summary Understanding proppant transport and distribution in hydraulic fractures is crucial to designing and optimizing hydraulic fracturing treatments in the field. The actual fracture surfaces are typically rough and form a tortuous pathway, significantly affecting proppant migration. However, many rough models are very small in size, and some have only one rough surface. Thus, it is inadequate to display proppant transport behaviors and placement laws. This study proposed a novel method to develop large-scale rough panels reproduced from actual hydraulic fractures. A large transparent slot (2×0.3 m) was successfully constructed to simulate a shear fracture with 5 mm relative displacement of two matched surfaces. Six kinds of proppants were selected to study the effects of particle density and size. Four types of slickwater were prepared to achieve viscous diversity. A high-resolution particle image velocimetry (PIV) system detected the instantaneous velocity and vector fields in the rough pathway to understand particle transport behaviors. The specific parametric study includes a quantitative analysis of the proppant bed profile, equilibrium height, coverage area, injection pressure, and volumes of proppant settled in the slot and outlet tank. Also, five tests are carried out in the smooth slot, which has the same size as the rough slot. The test results demonstrate that the narrow rough fracture would significantly hinder particle transport, especially in the horizontal direction. The proppant bed is higher and closer to the inlet than that in the smooth model. Particles mixed with highly viscous slickwater easily aggregate in the two-sided rough model and gradually form finger-like regions at the lower part of the inlet. The unstable flow and vortices can disperse aggregated particles and avoid particle clogging. Proppants injected at the high volume fraction are prone to settle quickly and build up a higher bed contact with the inlet, leading to more considerable injection pressure. Perforation blockage often occurred in the rough model, and the near-wellbore screenout was induced as the bed blocked all perforations. Enhancing the fluid carrying capacity and using smaller proppant help avoid perforation blockage and improve far-field fracture conductivity. Two correlations were developed to predict the equilibrium height and coverage area of the proppant bed. The experimental results and laws provide novel understandings that can help optimize hydraulic fracturing design and treatment by rationally selecting proppant and fracturing fluid to improve the productivity in tight reservoirs.

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