Presented at 50th U.S. Rock Mechanics/Geomechanics Symposium, Houston, Texas, June 2016.
Application of Artificial Intelligent Techniques to Determine Sonic Time from Well Logs
Geo-mechanical parameters are very important in petroleum industry. In order to obtain the geomechanical parameters, the sonic log (compressional and shear velocities) should be available. In many cases, the sonic log is not available or missing from the log data, for that cases the existing correlations are used to predict sonic time, most of the existing correlations use the compressional velocity to predict the shear velocity. The objective of this paper is to develop simple and accurate mathematical model to determine the compressional and shear sonic times using log data (gamma ray, bulk density, and neutron porosity). These three logs are commonly conducted at every well and they are always available. Three artificial intelligence techniques namely; ANNs (Artificial Neural Networks), ANFIS (Adaptive Neuro Fuzzy Inference System), and SVM (Support Vector Machines) are used. Finally, an attempt has also been made to converge the results into one simple empirical correlation using the weights of ANN model in order to make a generalized model that can be used for field applications. The results obtained showed that ANNs model successfully predict the compressional and shear sonic times from log data with 99% accuracy giving correlation coefficient of 0.99 when compared to actual field data.