Abstract
Treating every perforation cluster during a hydraulic fracturing treatment is a key element to ensuring the effectiveness and overall success of the well stimulation process. With completion costs being 50% of the total well cost in shale reservoirs and the reliance on hydraulic fracturing as a key technology for development, operators are concerned with maximizing the well performance by optimizing the stimulation design. In multi-stage horizontal well treatments, equal diversion of the fracturing fluid and proppant to all perforation clusters is desired, as bias proppant distribution may cause early screen-out and/or leave some fractures unpropped.
Computational fluid dynamics (CFD) is a branch of fluid mechanics that numerically solves Navier-Stokes equations and predicts the fluid flow behavior within a specified computational domain. CFD also has the capability to model the flow of dispersed particles within a fluid through the use of a discrete phase model (DPM). Recently, CFD has been utilized by various researchers as a tool to model fracturing slurry flow within perforation clusters to predict proppant transport and distribution trends.
This work models the fluid and particle flow using CFD DPM within a single hydraulic fracturing stage to improve our understanding of the proppant transport and distribution between multi-perforation clusters. The paper presents the CFD modeling methodology and results of two main perforation designs including traditional 0° phasing, vertical perforations and modified 45° angled shot perforations. To be able to compare the modeling to actual field conditions and field diagnostics, slickwater fracturing fluid with 40/70 and 100 mesh sand at a varying proppant concentration from 1 to 2 ppg are used in the model to replicate a field-scale 15-cluster stage. The modeling outcomes are then compared and validated to actual well post-job diagnostics; proppant tracers and perforation erosion signs captured by downhole cameras. These field results help to calibrate the CFD models, which then allowed sensitivity analysis and design improvement.
