Estimation of Shear Wave Velocity in one Iranian Hydrocarbon Reservoir Using Conventional Well logs and a New Intelligence Method


Department of Mining Engineering, Arak University of Technology, Iran


          Determination of shear wave velocity (Vs) by methods such as core analysis is time-consuming and costly. Also due to lack of sufficient core, lithology changes and heterogeneity of reservoir rock, did not have accurate determination by traditional methods. It also has plenty of empirical relationships is presented in the Vs, But in most cases it can be desirable to use this relationship in different areas. Intelligent methods using petrophysical reservoir predicted Vs in the shortest possible time. In this study, Vs was predicted from well logs data using support vector regression algorithm-based teaching and learning method in one Iranian hydrocarbon reservoir (Maroon Square). For this purpose, a total of 3800 data points were utilized. These  data were divided into two parts, one part included 3040 data points used for training models and the other part included 760 data points used for testing model. After modeling of  results data show that a new intelligence techniques were useful methods for prediction of Vs in reservoirs that this parameter has not been measured.


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