Data mining technology for machinery equipment and information networks

In order to predict the Rate of Penetration (ROP) during drilling, a new data mining technology for drilling ROP prediction is proposed. The weight of the ROP influencing factors determined by the Analytic Hierarchy Process (AHP) and the original data are used as the input information, after neural network training and iteration, the drilled ROP model is determined, finally, the model is used to simulate and predict the ROP to be drilled in the same area. Simulation results show that the Analytic Hierarchy Process-Back Propagation (AHP-BP) combined model established by the feedforward neural network principle can improve the iterative convergence speed and the reliability of the training results to a certain extent. Experimental results show that the predicted value of drilling speed is basically close to the actual value, the relative error is controlled within 10%, and the prediction effect is good with an accuracy of 98%.