A New Approach to Sand Production Onset Prediction Using Artificial Neural Networks

Sand production prediction has always been an important issue when dealing with production phenomena. Knowing all significant consequences of precise sand production prediction, different methods were developed using a variety of criteria and material models were implemented to obtain more accurate results. Although sand rate prediction has become a prevalent challenge nowadays, it does not reduce sanding onset prediction. Dealing with different methods and knowing the disadvantages of each one will clarify the necessity of developing a technique having the exactness and accuracy of numerical and experimental methods and simplicity of analytical ones. There was an endeavor in this article to apply powerful tools of an artificial neural network to predict critical bottomhole flowing pressure inhibiting sand production. Comprehensive well data gathered from 38 wells distributed in three oilfields producing from the same source rock were investigated to find the main parameters causing sand production. After verifying the proposed model with test wells, it was evaluated against well-accepted analytical models. The final results illustrate a reliable and more exact method that can predict sand initiation with a high degree of accuracy.