Achieving Near-Uniform Fluid and Proppant Placement in Multistage Fractured Horizontal Wells: A Computational Fluid Dynamics Modeling Approach

Summary Multistage plug-and-perforate fracturing of horizontal wells has proved to be an effective method to develop unconventional reservoirs. Various studies have shown uneven fluid and proppant distributions across all perforation clusters. It is commonly believed that both fracturing fluid and proppant contribute to unconventional well performance. Achieving uniform fluid and proppant placement in all perforation clusters is an important step toward optimal stimulation. This paper discusses how to achieve such uniform placement in each fracturing stage by means of a computational fluid dynamics (CFD) modeling approach. A laboratory-scale CFD model was built and calibrated using experimental data of proppant transport through horizontal pipes available from several laboratory configurations. A field-scale model was then built and validated using perforation erosion data from downhole camera observations. With the field-scale model validated, CFD simulations were performed to evaluate the impact of key parameters on fluid and proppant placement in individual perforations and clusters. Some key parameters investigated in this study included perforation variables (orientation, size, and number), cluster variables (count and spacing), fluid properties, proppant properties, pumping rates, and stress shadow effects. Both laboratory and CFD results show that bottom-side perforations receive significantly more proppant than top-side perforations because of gravitational effects. Laboratory and CFD results also show that proppant distribution is increasingly toe-biased at higher rates. Proppant concentration along the wellbore from heel to toe varies significantly. Gravity, momentum, viscous drag, and turbulent dispersion are key factors affecting proppant transport in horizontal wellbores. This study demonstrates that near-uniform fluid and proppant placement across all clusters in each stage is achievable by optimizing perforation/cluster variables and other treatment design factors. CFD modeling plays an important role in this design-optimization process.