Artificial Neural Network Modelling Enhances Prediction of Shear Wave Transit Time

Sonic log is the most versatile reservoir evaluation tool which has been introduced to the industry. Compaction, erosion and over pressurized zone can be evaluated by sonic log. Also primary porosity can be determined from compressional sonic wave transit time and secondary porosity will be calculated by comparing sonic derived porosity log with neutron and density based porosity log. On the other hand, all of the rock mechanical properties can be evaluated using simultaneous use of compressional and shear sonic wave transit time. Therefore it is essential to have shear velocity for conducting rock mechanical studies but in old wells no shear wave log could be found. This paper tries to highlight important role of shear wave velocity and numerous approaches to find that. Afterward, a network of neurons is built and shear transit time is evaluated. Results from network show very good match and accuracy in network’s predictions.

کلیدواژه ها:

Artificial Neyral Networks ، Sonic Log ، Shear Wave Transit Time ، Geomechanics