Proppant Distribution Among Multiple Perforation Clusters in Plug-and-Perforate Stages
- Sophie S. Yi (University of Texas at Austin) | Chu-Hsiang Wu (University of Texas at Austin) | Mukul M. Sharma (University of Texas at Austin)
- Document ID
- Society of Petroleum Engineers
- SPE Production & Operations
- Publication Date
- November 2018
- Document Type
- Journal Paper
- 654 - 665
- 2018.Society of Petroleum Engineers
- Proppant Transport Efficiency, DAS DTS, Heel Biased Proppant Distribution, Proppant Distribution among Multiple Clusters, Plug-and-Perf
- 14 in the last 30 days
- 522 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 5.00|
|SPE Non-Member Price:||USD 35.00|
Plug-and-perforate (Plug-and-Perf) fracturing stages with multiple perforation clusters have become common practice in the industry. However, it is usually unclear whether 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 multicluster 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 toward 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 with the PTE concept in three 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 multicluster 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 inertia sufficient to prevent them from turning into the perforations. Proppant concentration in the wellbore is thus increased as the slurry flows toward 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 early time in the proppant stage, fluid and proppant are redistributed 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 consistent with distributed-temperature-sensing (DTS) and distributed-acoustic-sensing (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 with this method. This can be used to optimize perforation clusters that will result in a more-even distribution of proppant in each cluster.
|File Size||1007 KB||Number of Pages||12|
Baihly, J., Malpani, R., Edwards, C. et al. 2010. Unlocking the Shale Mystery: How Lateral Measurements and Well Placement Impact Completions and Resultant Production. Presented at the SPE Tight Gas Completions Conference, San Antonio, Texas, 2–3 November. SPE-138427-MS. https://doi.org/10.2118/138427-MS.
Goniva, C., Kloss, C., Deen, N. G. et al. 2012. Influence of Rolling Friction on Single Spout Fluidized Bed Simulation. Particuology 10 (5): 582–591. https://doi.org/10.1016/j.partic.2012.05.002.
James, G., Witten, D., Hastie, T. et al. 2013. An Introduction to Statistical Learning: With Applications in R. New York: Springer. https://doi.org/10.1007/978-1-4614-7138-7.
Lafond, P. G., Gilmer, M. W., Koh, C. A. et al. 2013. Orifice Jamming of Fluid-Driven Granular Flow. Physical Review E 87 (4): 042204. https://doi.org/10.1103/PhysRevE.87.042204.
Miller C., Waters, G., and Rylander, E. 2011 Evaluation of Production Log Data From Horizontal Wells Drilled in Organic Shales. Presented at the SPE North American Unconventional Gas Conference and Exhibition, The Woodlands, Texas, 14–16 June. SPE-144326-MS. https://doi.org/10.2118/144326-MS.
Molenaar, M. M. and Cox, B. E. 2013. Field Cases of Hydraulic Fracture Stimulation Diagnostics Using Fiber Optic Distributed Acoustic Sensing (DAS) Measurements and Analyses. Presented at the SPE Unconventional Gas Conference and Exhibition, Muscat, Oman, 28–30 January. SPE-164030-MS. https://doi.org/10.2118/164030-MS.
Mondal, S., Wu, C.-H., and Sharma, M. M. 2016. Coupled CFD/DEM Simulation of Hydrodynamic Bridging at Constrictions. Int. J. Multiph. Flow 84: 245–263. https://doi.org/10.1016/j.ijmultiphaseflow.2016.05.001.
Romero, J., Mack, M. G., and Elbel, J. L. 1995. Theoretical Model and Numerical Investigation of Near-Wellbore Effects in Hydraulic Fracturing. Presented at the SPE Annual Technical Conference and Exhibition, Dallas, 22–25 October. SPE-30506-MS. https://doi.org/10.2118/30506-MS.
Roussel, N. P. and Sharma, M. M. 2012. Role of Stress Reorientation in the Success of Refracture Treatments in Tight Gas Sands. SPE Prod & Oper 27 (4): 346–355. SPE-134491-PA. https://doi.org/10.2118/134491-PA.
Sneddon, I. 1946. The Distribution of Stress in the Neighbourhood of a Crack in an Elastic Solid. Proc., the Royal Society of London A: Mathematical, Physical, and Engineering Sciences 187 (1009): 229–260. https://doi.org/10.1098/rspa.1946.0077.
Tran, T. V., Civan, F., and Robb, I. D. 2009. Correlating Flowing Time and Condition for Perforation Plugging by Suspended Particles. SPE Drill & Compl 24 (3): 398–403. SPE-120847-PA. https://doi.org/10.2118/120847-PA.
Ugueto, C., Gustavo, A., Huckabee, P. T. et al. 2016. Perforation Cluster Efficiency of Cemented Plug and Perf Limited Entry Completions; Insights From Fiber Optics Diagnostics. Presented at the SPE Hydraulic Fracturing Technology Conference, The Woodlands, Texas, 9–11 February. SPE-179124-MS. https://doi.org/10.2118/179124-MS.
Wheaton, B., Haustveit, K., Deeg, W. et al. 2016. A Case Study of Completion Effectiveness in the Eagle Ford Shale Using DAS/DTS Observations and Hydraulic Fracture Modeling. Presented at the SPE Hydraulic Fracturing Technology Conference, The Woodlands, Texas, 9–11 February. SPE-179149-MS. https://doi.org/10.2118/179149-MS.
Wu, C.-H. and Sharma, M. M. 2016. Effect of Perforation Geometry and Orientation on Proppant Placement in Perforation Clusters in a Horizontal Well. Presented at the SPE Hydraulic Fracturing Technology Conference, The Woodlands, Texas, 9–11 February. SPE-179117-MS. https://doi.org/10.2118/179117-MS.
Yi, S. and Sharma, M. 2016. A Model for Refracturing Operations in Horizontal Wells Employing Diverting Agents. Presented at the SPE Asia Pacific Hydraulic Fracturing Conference, Beijing, 24–26 August. SPE-181795-MS. https://doi.org/10.2118/181795-MS.