Lithofacies Classification in Barnett Shale Using Proximal Support Vector Machines

Zhao, Tao (University of Oklahoma) | Jayaram, Vikram (University of Oklahoma) | Marfurt, Kurt J. (University of Oklahoma) | Zhou, Huailai (Chengdu University of Technology)


Summary Classification of different lithofacies and petrotypes is one of the main objectives of modern quantitative seismic interpretation. In this application we illustrate the PSVM method to differentiate limestone from shale in a Barnett Shale gas play. The PVSM's low complexity feature compared to the standard vector machines could be well exploited in a data intensive computation such as the 3D seismic lithofacies classification. The paper reports two applications of this technique one for waveform classification and the other for the classification of well data. In both these applications PSVM classification results showed strong agreement with structural and stratigraphic interpretation results.