Formation Pressure Prediction from Mechanical and Hydraulic Drilling Data Using Artificial Neural Networks

Formation pressure gradient prediction is important in drilling operations from technical and economical points of view. The pressure data while drilling can be obtained by pressure while drilling (PWD) tool which is costly and not available in most wells. The available correlations for pore pressure prediction depend on well logging, formation properties, and combination of logging and drilling parameters. These data are not available for all wells in all sections. The objective of this paper is to use artificial neural networks (ANNs) to develop a model to predict the formation pressure gradient in real-time using both mechanical and hydraulic drilling parameters data. The used parameters included rate of penetration (ROP), mud flow rate (Q), standpipe pressure (SPP), and rotary speed (RS). A dataset of around 3,100 field data points were utilized to provide the predictive model. A different set of data (92 points) unseen by the model was utilized for validating the proposed model. The model predicted the pressure gradient with a correlation coefficient (R) of 0.98 and average absolute percentage error (AAPE) of around 2%.