APPLICATION OF PSO-LSSVM IN PREDICTION AND ANALYSIS OF SLOW DRILLING (RATE OF PENETRATION)

Abstract

The benefits of drilling include reducing the total time, maintaining the lowest possible risk, saving costs, and increasing efficiency, which occurs in (the planning and exploration stages). Slow drilling refers to a rate of penetration (ROP) that is not at the desired level. ROP characterizes the speed at which the drill bit penetrates the underlying rock to deepen the borehole, as it is directly related to controlling the speed and efficiency of drilling which ultimately impacts development costs. Predicting ROP is a very important step to optimize drilling with Machine Learning that can assist in solving complex problems with maximum possible efficiency. The model used is PSO-LSSVM treats the penetration drill bit as a continuous process. It takes drilling data sequentially, continuously predicts ROP, and achieves better ROP prediction results. In this case, Hole Depth, weight on bit (WOB), Bit Rotation per minute (RPM), Torque, Bit Depth, Time of Penetration, Hook Load, and Standpipe Pressure, demonstrated influence in keeping ROP at a high level. According to the results, the PSO-LSSVM algorithm can be used for the prediction of ROP in well X. thus providing a solution for prediction and control of operating effects which can result in a fast penetration rate and better efficiency in drilling.