New Analytical and Statistical Approach for Estimating and Analyzing Sand Production Through Plain Square-Mesh Screens During a Sand-Retention Test
- Rajesh A. Chanpura (Schlumberger) | Somnath Mondal (University of Texas at Austin) | Jamie S. Andrews (Statoil) | Anne-Mette Mathisen (Statoil) | Joseph A. Ayoub (Schlumberger) | Mehmet Parlar (Schlumberger) | Mukul M. Sharma (University of Texas at Austin)
- Document ID
- Society of Petroleum Engineers
- SPE Drilling & Completion
- Publication Date
- March 2013
- Document Type
- Journal Paper
- 135 - 147
- 2013. Society of Petroleum Engineers
- 2.4.4 Screen Selection, 3.2.5 Produced Sand / Solids Management and Control, 5.1 Reservoir Characterisation, 2.4.3 Sand/Solids Control
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- 529 since 2007
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There are two types of sand-retention tests (SRTs) generally used in the industry to evaluate the performance of sand-control screens for standalone-screen (SAS) applications--prepack tests and slurry tests. They represent complete hole collapse and gradual rock failure around the wellbore, respectively. In this paper, we present analytical models and Monte Carlo (MC) simulations to estimate sand production in slurry-type SRTs with square-mesh screens while taking into account the full particle- size distribution (PSD) of the formation sand. We also compare the model results with four sets of experimental data and demonstrate that this approach can be used to predict sand production for different sand-size-distribution/screen-size combinations without the need for physical tests (provided that both an accurate representation of the formation-sand PSD and an accurate representation of screen pore sizes are available). This work augments previously published slurry-test models that were limited to wire-wrapped screens (WWSs), and enables the comparison of the performance of square-mesh screens with WWSs. The analytical model and the MC simulations provide a direct and reliable way to estimate the amount of sand that will be produced for a given sand-size distribution and a given screen size. Because the proposed methods are much more quantitative, they represent a significant improvement that surpasses current methods that rely on single design points or rules of thumb for screen selection.
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