Application of artificial intelligence to predict sonic wave transit time in unconventional tight sandstones

Geomechanics plays a vital role in reducing drilling problems. Parameters such as Young’s modulus and Poisson’s ratio are calculated from acoustic logs (sonic wave transit time). Occasionally, these logs are not recorded because of many factors such as time saving or cost cutting. In such cases, empirical correlations are used to calculate the sonic transit times. But none of these empirical correlations are universally acceptable. As a result, inaccurate values can potentially raise major concerns throughout the life of the well. The objective of this paper is to develop a robust empirical model for compressional and shear wave transit times using artificial intelligence techniques for unconventional reservoirs. For this purpose, well logs data was used from a tight sandstone formation to predict the transit times. Artificial neural networks (ANN) was used in this study. The ANN models predicted the sonic waves with very high accuracy with a correlation coefficient (CC) up to 0.96 and an average absolute percentage errors (AAPE) as low as 2%. The shear wave transit time prediction from the new model was also validated using available sandstone empirical equations.