Rock drillability intelligent prediction for a complex lithology using artificial neural network

The fourth industrial revolution and its vision for developing and governing the technologies supported artificial intelligence (AI) applications in the different petroleum industry disciplines. Therefore, the objective of this paper is to use the artificial neural network (ANN) to build a model for the rate of penetration (ROP) that considers the effect of drilling parameters,formation lithology, and drill bit design on the ROP performance. The novelty in this study is addressing the influence of poly diamond crystalline (PDC) bit design as the number of blades and cutter size, bit nozzle total flowing area (TFA),and combined different drilled formations on the penetration rate. The well drilling data covered the 8-3/8" phase with more than 1000 readings for each input.The input data are the weight on bit (WOB),revolution per minute (RPM), torque (T), standpipe pressure (SPP),and mudflow rate (Q), mud weight (MWin), gamma-ray (GR), bit design codes as the number of blades and cutter size, bit nozzle, and total flowing area (TFA).The data training to testing ratio was 70: 30%. Another data set from the same filed was used to validate the model and the results showed high accuracy for the ANN-ROP model. The model provides a high performance and accuracy level with correlation coefficient (R) of 0.99, 0.98, and 0.98 and an average absolute percentage error (AAPE) of 4.36 %, 7.06 %, and 8.14 % for training, testing, and validating respectively.