## 1 Introduction  
The sand retention test (SRT) is a procedure often used to select the most optimal sand screen to be used in reservoir sand control [1]. Optimal sand screen selection refers to the selection of the most appropriate screen for sand control completion, minimizing sand production and maximizing hydrocarbon production. SRT simulates the sand production into a wellbore after passing through a filter, a standard test in the upstream sector in the oil and gas industry. It helps engineers choose the right screen and aperture size for the right field and environment. Implementing SRT helps in providing a better understanding of sand retention efficiency and plugging performance. It can be done in the laboratory or by simulation of mathematical models. Both results from laboratory and simulation are compared to examine the method's accuracy [2]. The SRT laboratory method that focuses on stand-alone screen (SAS) application is classified into two groups: the slurry test and the sand pack test [3]. Generally, the slurry test refers to an experiment where sand is suspended in a fluid to form a slurry, which is then pumped through the screen, while for the sand pack test, sand is placed directly onto the screen along with the confining stress to ensure that the sand is compressed with the screen before the fluid is pumped through the sand pack and the screen [4]. One of the experimental setups of slurry and sand pack tests are shown in Figure 1.  
However, the SRT problems observed from previous studies [1; 2; 3; 4; 5; 6; 7; 8; 9; 10] showed no standard guideline to carry out the SRT experiment. There are various experimental setups and different ways of interpreting the results. The plugging performance and sand retention efficiency are analyzed differently in selecting the most compatible SAS and screen slot size. A few changes made to the experimental setup can cause a huge variation in results. Besides, conducting many laboratory experiments is expensive and time-consuming [11]. Testing one particular screen and slot size to analyze the sand production can take hours to reach the final result despite using the laboratory or simulation tests. Deep learning is a sub-branch of machine learning inspired by biological neurons of the brain called artificial neural networks (ANNs) with representation learning. It highlights the use of multiple connected layers with a dominant learning ability to take inputs as the features, which are modelled to predict outputs [12]. Deep learning has become the most vital discovery and research hotspot, which can tackle intricate patterns in a massive dataset of any data type [13]. It is not only implemented to model the prediction or classification problem, but it can also be applied for modelling forecasting time-series data [14; 15; 16; 17; 18; 19; 20]. It can automatically apply feature extraction where a separate implementation is needed for shallow machine learning approaches. Convolutional neural networks (CNNs) is a specific type of ANN that focus on many deep layers of neural networks, which uses convolutional and pooling strategies [21]. It is a deep learning model used primarily in computer vision problems, such as image classification [22], image segmentation [23], and video object segmentation [24], where it  
Figure 1: Sand retention test (SRT) experimental setup [4]: (**a**) Slurry test; (**b**) Sand pack test. shows promising results. It can also be used for regression problems to generate complex models with higher accuracy using complex datasets, which cannot be done with just a simple regression function. CNN typically uses two- or three-dimensional neural layers for classification problems that take the input in the same dimension to undergo a feature learning process. It works the same way despite having one, two, or three dimensions. The differences between dimensions are the input structure, pool size, filter size, and how it shifts from features to features. Developing CNN starts with a configuration of the external parameters known as hyperparameters [25].