Prediction of NCPSO-BP ROP based on principal component analysis

The currently and commonly used theoretical models predicting rate of penetration (ROP) only screen the model input parameters through correlation and contribution, which lacks active exploration on the relationship between complex attributes collected while drilling, resulting in a lack of completeness of information. In order to maximize the preservation of the linear relationship between complex attributes, a drilling speed prediction model based on principal component analysis was proposed. And by optimizing the BP neural network with niche particle swarm algorithm after introducing chaotic variation, the convergence speed and the accuracy of the model were improved. Firstly, with the help of the principal component analysis method, the dimension and the noise of the high-dimensional drilling data were reduced according to different variance contributions. Secondly, an intelligent optimization algorithm-neural network drilling speed prediction model was established, and using the training results from the niche particle swarm algorithm after introducing chaotic variation to give initial values to the weights and the thresholds of the BP neural network, so as to establish the ROP prediction model. Finally, the ROP prediction results between NCPSO-BP model and PSO-BP, as well as the results between GA-BP and standard BP in different input dimensions were compared and analyzed. The results show that in the case of 8-dimensional and 10-dimensional inputs, the prediction accuracy of the NCPSO-BP ROP model was increased by an average of 59%, and the training speed was increased by an average of 26.3%. The NCPSO-BP ROP model can provide a theoretical basis for accurate prediction of ROP in increasingly complex drilling environments.