Prediction of ROP by Machine Learning and its Application in Engineering

Rate of penetration (ROP) is a key performance indicator in drilling engineering, which is directly related to drilling cost. The accurate judgment of ROP is the key of drilling engineering decision. To solve the problems of large error and low effective of traditional ROP prediction methods, this paper attempts to obtain a more accurate and reliable ROP prediction model through machine learning method. Different machine learning methods are used to preliminarily establish the prediction model of ROP, and then the gradient lifting tree algorithm is selected for predicting ROP through performance comparison. Finally, the prediction model of ROP is established by optimizing parameters. The trained prediction model is applied to an exploration well in a basin in South China Sea, and the prediction results are in line with the reality. The results predicted by the model can be used to evaluate the ROP, identify the abnormal ROP, and provide a basis for engineering decision-making.