The installation of sand screens in open-hole completions in the wellbore is crucial for managing sand production. The main reason for using standalone screens in open-hole completions is their relatively reduced operational complexity compared to other sand control technologies. However, directly applying the screen to the bottom of the hole can lead to an incorrect screen type selection, resulting in an unreliable sand control method. To address this issue, a sand retention test is conducted to evaluate the performance of a standalone screen before field installation. Nevertheless, current sand retention test setups encounter several challenges. These include difficulties in identifying minimum retention requirements, interpreting results in the context of field conditions, and replicating field-specific parameters. The existing sand retention test introduces uncertainties, such as inaccurately replicating field requirements, inconsistent selection of wetting fluids, flow rates, and channel formation, leading to variations in the choice of the optimal screen using this test. In response to these challenges, this study aims to review the sand retention test and propose an improved sand retention method to overcome these problems. The focus of this article is to provide an in-depth analysis of previous sand retention test setups, their contributions to characterizing sand screens, and the parameters utilized in determining test outcomes. Additionally, this review outlines a procedure to investigate the impact of different particle sizes on screen erosion. Key findings emphasize the importance of using high-quality materials, proper screen design to resist damage and erosion, achieving acceptable natural packing behind the screen, and considering factors such as geology, wellbore conditions, and installation techniques. The analysis reveals that a high quantity of finer and poorly sorted sand increases sand production. The study recommends performing a sand pack test closer to reservoir conditions for better evaluation. Premium sand screens demonstrate the highest retention capacity, followed by metal mesh and wire-wrapped screens. Additionally, geotextiles show potential for enhancing sand retention, and screen design affects erosion resistance and service life.

Stand-alone screens (SASs) are active sand control methods where compatible screens and slot sizes are selected through the sand retention test (SRT) to filter an unacceptable amount of sand produced from oil and gas wells. SRTs have been modelled in the laboratory using computer simulation to replicate experimental conditions and ensure that the selected screens are suitable for selected reservoirs. However, the SRT experimental setups and result analyses are not standardized. A few changes made to the experimental setup can cause a huge variation in results, leading to different plugging performance and sand retention analysis. Besides, conducting many laboratory experiments is expensive and time-consuming. Since the application of CNN in the petroleum industry attained promising results for both classification and regression problems, this method is proposed on SRT to reduce the time, cost, and effort to run the laboratory test by predicting the plugging performance and sand production. The application of deep learning has yet to be imposed in SRT. Therefore, in this study, a deep learning model using a one-dimensional convolutional neural network (1D-CNN) with adaptive moment estimation is developed to model the SRT with the aim of classifying plugging sign (screen plug, the screen does not plug) as well as to predict sand production and retained permeability using a varying sand distribution, SAS, screen slot size, and sand concentration as inputs. The performance of the proposed 1D-CNN model for the slurry test shows that the prediction of retained permeability and the classification of plugging sign achieved robust accuracy with more than a 90% value of R², while the prediction of sand production achieved 77% accuracy. In addition, the model for the sand pack test achieved 84% accuracy in predicting sand production. For comparative model performance, gradient boosting (GB), K-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM) were also modelled on the same datasets. The results showed that the proposed 1D-CNN model outperforms the other four machine learning models for both SRT tests in terms of prediction accuracy.

Understanding the behavior of sand screens is crucial for optimizing sand control strategies and preventing wellbore failure, which can significantly impact reservoir management and production efficiency. This paper presents a comprehensive experimental and numerical modeling study on sand screen performance, aimed at providing insights prior to real-field applications. The study evaluated a 200-μm wire-wrapped screen (WWS) using slurry tests to determine the amount of sand retained, sand produced and retained permeability to assess screen efficiency. The particle size distribution (PSD) of sand samples was crucial for selecting the appropriate screen size, with results showing that highly uniform and well-sorted sand exhibited higher sand retention and lower sand production due to reduced fines content and maximized void spaces, enhancing fluid flow while mitigating sand production. A coupled Computational Fluid Dynamics-Discrete Element Method (CFD-DEM) simulation platform was employed to model particle-fluid interactions, using an optimal mesh size of 0.005 m. The simulation results showed a high degree of correlation with experimental data, with 93.1 % similarity in sand retention and 63.22 % in sand production. These findings underscore the potential of CFD-DEM simulations in accurately replicating complex sand control processes and optimizing screen performance for enhanced wellbore stability and production efficiency.