Discussion on the application of artificial intelligence method to the prediction of drilling machinery ROP

Artificial intelligence methods are widely used to predict the rate of penetration in the drilling process. Although the prediction accuracy can exceed 80%, most of the previous algorithms only select the data from one well or one drilling section for prediction and inspection, lacking the research on the generalization and prediction of adjacent wells or the whole block. Therefore, the generalization ability of these algorithms needs to be tested. In view of the above problems, the influence of correlation analysis on drilling parameter selection during prediction the rate of penetration and the influence of training data selection on the generalization ability of artificial intelligence models were discussed. The formation parameters, drill bit parameters and drilling parameters were introduced as input parameters, and the actual drilling data of a block in the Sichuan Basin was selected for training. The accuracy of 4 artificial intelligence algorithms, that is random forest, support vector machine, gradient boosting tree and artificial neural network, were evaluated for predicting the penetration rate of the entire block. The results show that the prediction accuracy of the random forest algorithm for the data of each single well in the block can reach 90%, and the prediction accuracy for the data of the whole block can reach 88%. The random forest model trained with the block data has better generalization ability, it is believed that this method can be extended to the whole block, which is beneficial to guide the optimization of drilling engineering technology in this block.