Development of an Optimal Model For Rate of Penetration Rop Using Deep Neural Networks DNN.

Abstract For the past century, optimization of drilling has caught the eyes of many researchers. The main areas center on ROP, fluid treatment, and bit selection. They all share the same goal of maximizing ROP and reducing NPT. In other to develop an optimal control system, ROP must be predicted accurately, unfortunately, it is a complex parameter that is affected by multiple drilling parameters, rock properties, fluid properties, and bit selection. Models used for prediction have developed from empirical models like Bourgoyne and Young's to more intelligent models such as SVM and ANN. With the continuous increase in data obtained from sensors while drilling, there is still much work to be done in this field. In this research, the improvement of an empirical model and the development of an intelligent model are presented. The Bourgoyne and Young's model uses multiple linear regression to estimate coefficients which it then inserts into an empirical formula to predict ROP. This model was modified using non-linear curve-fitting to estimate the coefficients and make it reduce bias to generalize better. Machine learning models such as Gradient Boosting, Random Forest, ANN, and DNN were used in the development of a predictive model for the ROP. These models were easier to develop compared to the empirical model since they rely more on data rather than statistical formulas. The data used in this research include drilling data from 3 wells drilled in 2 fields within the Niger Delta region in Nigeria. The models were developed and trained on one of the wells, while the remaining two were used for testing the performance of the models. The modified empirical model improved the efficiency of the base model by 14% during validation but performs poorly on unseen data from the other two wells. The Machine learning models outperform the empirical models and perform accurately on unseen data from the other wells. DNN was the best performing model achieving an average accuracy of 0.987 for the 3 wells.