Efficient Prediction of Proppant Placement along a Horizontal Fracturing Stage for Perforation Design Optimization

Abstract

Summary Multistage plug and perforation (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 proved to be challenging with low viscosity slickwater owing 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 multicluster 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 proppant inertia effect is a key factor affecting proppant placement in heel clusters with higher slurry flow rates, allowing more proppant carried to the toe owing to its higher density in comparison with fluid. Proppant settling allows bottom perforations to accept more proppant than top perforations. This gravitational effect is not negligible near the heel at high flow rates and becomes more dominant near toe clusters where the flow rate is reduced. 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 some of the limitations from existing models in the literature and is able to provide quick and yet reliable solutions to proppant placement prediction and design optimization.

A model for proppant dynamics in a perforated wellbore

“…The displacement of the latter slip is denoted by s. Note that the fact that proppant located only near the perforation leaves the wellbore was observed in [19] via CFD simulations and the area outlined by l p was called the "ingestion area". Similarly, authors in [18] plotted the fluid and proppant streamlines, which confirmed that the "ingestion" area is nearly circular and that there is a difference between those areas for fluid and proppant, as outlined by l f and l p . Fig.…”

“…While the system of equations (18) gives the complete or full model, it requires solving a two-dimensional equation, which needs significantly more computational effort than classical wellbore simulation approaches. Therefore, the purpose of this section is to outline a reduced model, that is based on the steady-state solution (19), but yet still captures the dominant effect of time dependence.…”

“…This is perhaps the first study that explicitly outlined that the problem of slurry flow in a perforated wellbore has two sub-problems, one related to the non-uniform particle distribution in the wellbore, and one related to proppant turning. Also, the study [18] pointed out that fluid streamlines located closely to perforation enter the perforation. At the same time, only a fraction of proppant streamlines located within this zone enters the perforation.…”

“…One interesting observation made in the paper is that particle concentration profile in the wellbore is non-uniform in general and changes from nearly uniform for the first cluster to strongly non-uniform for the last cluster. Another CFD modeling study [18] focused on the developing of machine learning algorithm to have an ability to quickly predict particle distribution for various configurations. This is perhaps the first study that explicitly outlined that the problem of slurry flow in a perforated wellbore has two sub-problems, one related to the non-uniform particle distribution in the wellbore, and one related to proppant turning.…”

“…The modeling effort of the problem is relatively unfilled. As was mentioned before, there are several sucessfull attempts to develop proxy models based on fitting [18,6]. One available model that stands up a little is presented in [23].…”