A slurry-type sand-retention test (SRT) that simulates gradual rock failure around the wellbore has been used widely in the industry to evaluate the performance of sand-control screens for standalone-screen (SAS) applications. Using the test results, screen selection is achieved generally on the basis of the relative ranking of screen performances rather than absolute performance.

Chanpura et al. (2011) highlighted recently the drawbacks of the current practices in slurry-type SRT procedures and proposed a new testing and interpretation methodology. Mondal et al. (2011) proposed simulation methods and results that, to the best of our knowledge, modeled screen performance numerically for the first time and presented comparisons to physical experiments. However, the approach used by Mondal et al. (2011) considers cases in which hole collapse occurs on wire-wrapped screens (WWSs) and simulates "prepack" testing as opposed to the slurry-type tests considered in this work.

In this paper, we present an analytical and a numerical [Monte Carlo (MC)] approach for the prediction of sand production through sand screens with slot geometry. We show that the proposed methods can estimate both mass and size distribution of the produced solids in a slurry-type SRT, taking into account the full particle-size distribution (PSD) of formation sand for WWSs. Simulations show that once the slot opening is covered by particles larger than the slot opening, sand production becomes negligible unless there is a true "fines" problem, which is characterized by a bimodal size distribution. The effect of slot-size variation in screen coupons on sand production demonstrates the importance of proper quality control or at least accurate determination of slot sizes in these tests. The proposed methods can be used to estimate sand production in slurry-type SRTs for different screen sizes and thereby can enable screen-size selection on the basis of a defined acceptable level of sand production. Final screen selection can be confirmed through an SRT.