Real-time prediction of rate of penetration while drilling complex lithologies using artificial intelligence techniques

Predicting the rate of penetration (ROP) plays a key role in the success of the drilling operation. It is not an easy task to predict the ROP with high accuracy as it depends on several factors such as; drilling parameters, drilling fluid properties, and drilled formation characteristics. The objective of this paper is to develop a new empirical equation for predicting the ROP in real-time using different artificial intelligence (AI) techniques such as artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM). For the first time, poly diamond crystalline (PDC) bit design parameters, total flow area, in addition to mud density (MWin), gamma ray (GR), and drilling parameters were used to build the AI models. Actual field data was used to build the AI models (1000 data points from Well A) and another 972 data points from Well B were used for validating the developed AI models. The obtained results confirmed that the three AI techniques could be used to predict the ROP for complex lithologies with high accuracy. The ANN outperformed the SVM and ANFIS for predicting the ROP for the unseen data (972 data points of validation). The developed ROP-ANN model could be used to predict the ROP with high accuracy (the root mean square error (RMSE) was less than 0.659 for the available two wells). The developed empirical correlation was able to predict the ROP with high accuracy, RMSE was 0.66. The new ROP equation can be used without the need for the ANN Matlab code or special software.