Experimental and 3D Numerical Investigation on Proppant Distribution in a Perforation Cluster Involving the Artificial Neural Network Prediction

作者：Hai Qu, Xiangjun Chen, Jun Hong, Yang Xu, Chengying Li, Zhelun Li, Ying Liu · 发表于：SPE Journal · 年份：2023 · DOI：10.2118/214316-pa · 被引用次数：22 · 研究领域：Hydraulic Fracturing and Reservoir Analysis、Drilling and Well Engineering、Oil and Gas Production Techniques

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 ...