Abstract:When using swirling abrasive water jet to drill casing windows in the wellbore, the size and the depth of the casing windows are two key parameters of the operation. In practice, it is quite difficult to monitor the geometry of the casing window in real-time. Therefore, it is essential to make an accurate prediction of the quality of the penetration performance so as to optimize the process parameters. There are a lot of factors influencing the casing perforation performance, as a result, it is not realizable to make predictions with conventional mathematical model. To address this issue, in this paper a BP-neural network predictor model is proposed to investigate the quality of the casing window. In addition, the BP-neural network predictor is corrected using genetic algorithm to achieve better results and it is verified with some experimental data. The test results turned out to be satisfying with the margin of error under 10%. This technology has proven itself one of the most reliable methods and certainly meets the demands of the industry.