Optimization of Plug-and-Perf Completions for Balanced Treatment Distribution and Improved Reservoir Contact
- Sophie Yi (The University of Texas at Austin, now with Pioneer Natural Resources) | Chu-Hsiang Wu (The University of Texas at Austin) | Mukul Sharma (The University of Texas at Austin)
- Document ID
- Society of Petroleum Engineers
- SPE Hydraulic Fracturing Technology Conference and Exhibition, 5-7 February, The Woodlands, Texas, USA
- Publication Date
- Document Type
- Conference Paper
- 2019. Society of Petroleum Engineers
- 2.1 Completion Selection and Design, 2.1.3 Completion Equipment, 2.1 Completion Selection and Design, 2 Well completion, 2.2 Installation and Completion Operations, 2.5.2 Fracturing Materials (Fluids, Proppant), 4 Facilities Design, Construction and Operation, 2.2.2 Perforating, 4.1.2 Separation and Treating, 4.1 Processing Systems and Design, 2.4 Hydraulic Fracturing
- Plug-and-Perf Treatment Distribution, Proppant Transport Efficiency (PTE), Automated Optimization of Plug-and-Perf Design, Multi-Fracture Model, Suggestions for Perforation and Pumping Schedule
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- 702 since 2007
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Heel-dominated treatment distribution among multiple perforation clusters is frequently observed in plug-and-perf stages, causing small propped surface areas, suboptimal production, and unexpected frac-hits.
A multi-fracture simulator with a novel wellbore fluid and proppant transport model is applied to quantify treatment distribution among multiple perforation clusters in a plug-and-perf operation. A simulation Base Case is set up based on a field treatment design with four clusters. Simulation results show that the two toe-side clusters screened out early in the treatment and the two heel-side clusters were dominant. The simulated proppant placement is consistent with DAS observations.
The impact of different perforating strategies and pumping schedules on final treatment distribution is investigated. Two criteria are defined that quantify the proppant distribution and fracture area: the Weighted Average (WA) and Standard Deviation (SD) of the final fluid and proppant distribution, as well as the Hydraulic and Propped Surface Area (HSA and PSA) of the created fractures. An optimum plug-and-perf design is defined as one that minimizes the SD of the treatment distribution among perforation clusters and maximizes the PSA.
Both perforating strategy and pumping schedule are found to affect the final treatment distribution significantly, and uniform treatment distribution is shown to create more PSA. Fewer perforations-per-cluster were found to promote uniform fluid and proppant placement. Other helpful strategies include reducing the number of perforations near the heel, using small, lightweight proppant and so on. The stress shadow effect is accounted for using the Displacement Discontinuity Method (DDM) and was found to play a smaller role than perforation friction and proppant inertia in most cases.
An automated process is developed to optimize plug-and-perf completion design with multiple decision variables using a Genetic Algorithm. Thirteen parameters are optimized simultaneously. The optimal design solution creates an almost even treatment distribution and more than doubled the PSA compared to the Base Case.
The multi-fracture model presented in this paper provides a way to quantify fluid and proppant distribution for any perforating strategy and pumping schedule and provides more insights of the physics relevant to plug-and-perf treatment distribution. The perforation and pumping schedule recommendations presented in this paper provide directional guidance to design a fracturing job of balanced treatment distribution and large propped surface area.
|File Size||1 MB||Number of Pages||21|
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