Summary Proppant placement plays a crucial role in ensuring that the conductivity of fractures is maintained after a hydraulic-fracturing treatment. The process involves the transport of suspended solids in a liquid, usually a water-based fluid, from the wellbore through perforations and finally into fractures. Many studies have focused on proppant settling and transport in fractures, but relatively few studies have investigated the proppant transport process in a wellbore through perforations. This paper addresses the important issue of proppant transport through perforations using a novel numerical technique. The objective is to evaluate the efficiency of proppant transport in a perforated horizontal well under different suspension flow conditions. In this paper, proppant transport through a perforated horizontal casing is modeled using a coupling of computational fluid dynamics and the discrete element method (CFD-DEM). Reasonable agreements are found between the modeling results and published experimental data. Furthermore, the effectiveness of proppant transport through a perforation is evaluated by the particle transport efficiency (Ei), which is defined as the mass fraction of particles transported through a perforation relative to the total mass of particles in the wellbore upstream of the perforation. The effects of casing diameter, proppant size, proppant density, proppant concentration, fluid-flow rate, fluid rheology, perforation size, and perforation orientation on Ei are investigated. The simulation results show that proppant inertia strongly influences proppant transport into a perforation. The proportion of proppant that goes into a perforation is typically much different than the proportion of fluid that goes into the same perforation. This results in an increase in the proppant concentration in the slurry as the slurry flows from the heel to the toe side of a plug-and-perforate stage. Results and models presented in this paper provide directions to quantify and potentially control proppant distribution in perforation clusters in horizontal wells.
Plug-and-perf fracturing stages with multiple perforation clusters have become common practice in the industry. However, it is usually unclear whether or not the fluid and proppant are distributed evenly among all clusters. In this study we present a method for computing the proppant distribution into each cluster in a fracturing stage. By integrating proppant transport into a multi-cluster hydraulic fracturing model and implementing a simple screenout criterion, we show that the proppant distribution in a fracturing stage can be very uneven with a strong bias towards the heel-side clusters even when the initial fluid distribution is uniform among all clusters. In this work, we define the efficiency of proppant transport into a perforation by the proppant transport efficiency (PTE), which is defined as the mass fraction of proppant transported through a perforation relative to the total mass of proppant approaching the perforation. The dynamic proppant distribution in a fracturing stage is modeled using the PTE concept in 3 steps. First, a series of coupled computational fluid dynamics - discrete element method (CFD-DEM) simulations were performed to obtain PTE under controlled flow conditions. Then, the CFD-DEM simulation results were statistically analyzed to generate a PTE correlation as a function of wellbore, perforation, fluid, and proppant properties. Finally, the PTE correlation was incorporated into a multi-cluster hydraulic fracturing model to compute the dynamic distribution of fluid and proppant among multiple clusters in a fracturing stage. Results from this work show that proppant concentration in the toe-side clusters can be several times higher than the injected concentration. This occurs because the high wellbore flow rate near the heel-side clusters provides proppant particles a large enough inertia that prevents them from turning into the perforations. Proppant concentration in the wellbore is thus increased as the slurry flows towards the toe side and the fluid preferentially leaks off from the heel side perforations. The highly concentrated slurry increases the screenout risk of the toe-side clusters. Our modeling results show that if toe-side clusters screen out at an early time of a proppant stage, fluid and proppant are re-distributed to the heel-side clusters. In such a case, cumulative fluid and proppant distributions will be heel-biased. Simulation results are compared with field observations and are shown to be completely consistent with DTS and DAS observations on proppant distribution made in three different studies. The method presented in this work provides a way to quantify proppant transport at a wellbore scale. It shows that the uneven proppant distribution among perforation clusters is a function of fluid, perforation and proppant properties. An estimate of proppant placement in different perforation clusters can be computed for any pumping schedule and wellbore/perforation geometry using this method. This can be used to optimize perforation clusters that will result in a more even distribution of proppant in each cluster.
Proppant placement plays a crucial role in ensuring that the conductivity of fractures is maintained after a hydraulic fracturing treatment. The process involves the transport of solids suspended in a liquid (usually a water-based fluid) from the wellbore through the perforations and finally into the fractures. Many studies have focused on proppant settling and transport in fractures but relatively few studies have investigated the transport of slurries through perforations into the fracture. The paper addresses the important issue of proppant transport through perforations by modelling the fundamental physics involved in the process. The objective of this paper is to evaluate the efficiency of proppant transport in a perforated horizontal well under different suspension flow conditions. In this paper, proppant transport through a perforated horizontal casing is modelled using a combined CFD-DEM approach. The CFD-DEM model results are compared with experimental data and excellent agreement is observed. The effectiveness of proppant transport is evaluated by the particle transport efficiency (Ei), which is defined as the mass fraction of particles transported through the perforations relative to the total mass of particles injected. The effects of changing casing diameter, proppant size, proppant density, proppant concentration, fluid flow rate, fluid rheology, perforation size, and perforation orientation on Ei are investigated. The results show that the perforation orientation has a large influence on Ei at low wellbore flow rates (typically seen in the downstream perforations). Under such conditions, proppant concentration in a low- side perforation is always larger than the upstream wellbore proppant concentration while proppant concentration in a high-side or a side perforation is always smaller than the upstream wellbore concentration. Increasing proppant size, proppant density, or decreasing fluid viscosity leads to an increase in Ei for low-side perforations and a decrease in Ei for high-side perforations. Increasing fluid flow rate and fluid viscosity helps to provide a more consistent Ei for perforations with different orientations; the proppant concentration in perforations in both scenarios are, however, smaller than the upstream proppant concentration regardless of perforation orientation. An increase in wellbore proppant concentration is found to have a negative effect on Ei for perforations of all orientations. An increase in perforation size is found to increase Ei for low-side and side perforations at a low flow rate but shows an insignificant effect on Ei for a high-side perforation. Finally, the usage of cross-linked gel provides a more consistent Ei among differently oriented perforations. However, even when gel is used, the proppant concentration in the perforation is still lower than the wellbore concentration, and it decreases as the flow rate increases. Results from this paper enhance our understanding of proppant transport from the wellbore into fractures and provide guidelines for engineers to follow to better control proppant distribution in perforation clusters in horizontal wells.
Sand particle size distributions (PSD) are used for various purposes in sand control: decision between various sand control techniques, sizing of the filter media (sand screens and/or gravel packs) through either rules of thumb or physical experiments or theoretical models. PSD of formation sand samples are also often used to generate "simulated" formation sand for laboratory experiments. The two most commonly used techniques for PSD measurements are sieve and laser, while some engineers use one technique for no obvious or justifiable reasons, others use both techniques for measurements and don't know what to do with the data when significant differences exist in PSDs obtained from each technique. Although the inherent limitations of, and the differences between, these two techniques as well as other factors impacting the measurements are well known, a systematic study as to what is relevant to sand control and why, is lacking. In this paper, we critically review the current practices in PSD determination, use (and misuse) of the information obtained from these measurements, propose a methodology towards determining what is relevant, when and why, and present initial experimental results that support our conclusions.
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