Support vector regression based determination of shear wave velocity

Shear wave velocity in the company of compressional wave velocity add up to an invaluable source of information for geomechanical and geophysical studies. Although compressional wave velocity measurements exist in almost all wells, shear wave velocity is not recorded for most of elderly wells due to lack of technologic tools in those days and incapability of recent tools in cased holes. Furthermore, measurement of shear wave velocity is to some extent costly. This study proposes a novel methodology to remove aforementioned problems by use of support vector regression tool originally invented by Vapnik (1995, The Nature of Statistical Learning Theory. Springer, New York, NY). Support vector regression (SVR) is a supervised learning algorithm plant based on statistical learning (SLT) theory. It is used in this study to formulate conventional well log data into shear wave velocity in a quick, cheap, and accurate manner. SVR is preferred for model construction because it utilizes structural risk minimization (SRM) principle which is superior to empirical risk minimization (ERM) theory, used in traditional learning algorithms such as neural networks. A group of 2879 data points was used for model construction and 1176 data points were employed for assessment of SVR model. A comparison between measured and SVR predicted data showed SVR was capable of accurately extract shear wave velocity, hidden in conventional well log data. Finally, a comparison among SVR, neural network, and four well-known empirical correlations demonstrated SVR model outperformed other methods. This strategy was successfully applied in one of carbonate reservoir rocks of Iran Gas-Fields.