Real-Time Prediction of the Dynamic Young’s Modulus from the Drilling Parameters Using the Artificial Neural Networks

Abstract

The dynamic Young’s modulus (Edyn) is an important factor for evaluating the static Young’s modulus which is an important parameter influences hydraulic fracturing design, drilling performance, in situ stresses estimation, and wellbore stability evaluation. Currently, the prediction of the Edyn requires the knowledge of the shear and compressional velocities and bulk density, which may not always be available. For the first time, an equation for evaluating Edyn in real-time from the weight on bit, the rate of penetration, torque, standpipe pressure, drill pipe rotation speed, and the drilling fluid flow rate is introduced. This new correlation was developed from the artificial neural networks (ANN) which were trained on 2054 data points from Well-A. The developed correlation was tested and validated on 871 and 2912 data points from Well-B and Well-C, respectively. The results showed that the ANN model evaluated Edyn with average absolute percentage error (AAPE) of 3.09%, for the training data. The developed equation estimated the Edyn with AAPE of only 3.38% and 3.73%, for the testing and validation data, respectively.