@inproceedings{scholars11847,
            note = {cited By 1; Conference of International Petroleum Technology Conference 2019, IPTC 2019 ; Conference Date: 26 March 2019 Through 28 March 2019; Conference Code:146421},
           title = {Fluid discrimination using bulk modulus and neural network},
            year = {2019},
         journal = {International Petroleum Technology Conference 2019, IPTC 2019},
       publisher = {International Petroleum Technology Conference (IPTC)},
             doi = {10.2523/iptc-19317-ms},
        keywords = {Elastic moduli; Forecasting; Gas industry; Gasoline; Hydrocarbons; Lithology; Neural networks; Petroleum industry; Stochastic systems, Attributes; Hydrocarbon predictions; Learning approach; Oil and Gas Industry; Rock physical; Strong correlation, Deep learning},
        abstract = {Hydrocarbon prediction using the rock physical parameters is a common technique in the oil and gas industry. However, the rock physical parameters are controlled by porosity, the volume of clay, pore-filled fluid type and lithology simultaneously. Many methods are proposed to predict the existence of hydrocarbon. This paper proposes a new method {\^I}?K which is the difference between the real bulk modulus and the bulk modulus in the brine- substitute case. The algorithm is validated through stochastic numerical modelling. The brines are separated by the {\^I}?K, and the gas can be detected with acceptable accuracy. Furthermore, a model using deep learning approach is trained to predict the {\^I}?K. The trained model is effective that the predicted values using this model have a strong correlation with the original {\^I}?K. The {\^I}?K can be applied to the data which contains Vp, Vs and density using this approach model. In this study, the {\^I}?K is applied to the Marmousi II dataset to examine the performance and yields a good result. The combination of the deep learning and the {\^I}?K improves our ability in hydrocarbon prediction. {\^A}{\copyright} 2019, International Petroleum Technology Conference},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126503814&doi=10.2523\%2fiptc-19317-ms&partnerID=40&md5=6500bfb62cdc286b220334932ca35ac5},
            isbn = {9781613996195},
          author = {Liu, C. and Ghosh, D. and Salim, A. M. A. and Chow, W. S.}
}