New Model for Pore Pressure Prediction While Drilling Using Artificial Neural Networks

Pore pressure is one of the main formation conditions that affects the efficiency of drilling operations and impacts its cost. Accurate prediction of the pore pressure and the parameters controlling it will help reduce the drilling cost and avoid in some cases catastrophic accidents. Many empirical models reported in the literature were used to predict the pore pressure based on either drilling parameters or log data. Empirical models require trends such as normal or abnormal pressure to predict the pore pressure. Few researchers applied artificial intelligence (AI) techniques to predict the pore pressure using one or maximum two AI methods (which are black box). There is no developed empirical correlation for pore pressure prediction based on optimized AI techniques. The objective of this paper is to predict the pore pressure based on both drilling parameters and log data, namely weight on bit (WOB), rotary speed (RPM), rate of penetration (ROP), mud weight (MW), bulk density (RHOB), porosity (phi), and compressional time (t). Real field data will be used to predict the pore pressure using artificial neural network (ANN). Finally, for the first time, a new empirical correlation for pore pressure prediction will be developed based on the optimized ANN model. The obtained results showed that it is very important to combine the drilling parameters and the logging data to predict the pore pressure with a high accuracy. A new empirical correlation was developed using the optimized ANN method that can estimate pore pressure with high accuracy (correlation coefficient of 0.998 and average absolute percentage error of 0.17%). Unlike the published empirical models, the new model requires no prior pressure trends (such as normal or abnormal pressures) to perform prediction.