TY - CONF N1 - cited By 1; Conference of International Petroleum Technology Conference 2019, IPTC 2019 ; Conference Date: 26 March 2019 Through 28 March 2019; Conference Code:146421 Y1 - 2019/// N2 - 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 Î?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 Î?K, and the gas can be detected with acceptable accuracy. Furthermore, a model using deep learning approach is trained to predict the Î?K. The trained model is effective that the predicted values using this model have a strong correlation with the original Î?K. The Î?K can be applied to the data which contains Vp, Vs and density using this approach model. In this study, the Î?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 Î?K improves our ability in hydrocarbon prediction. © 2019, International Petroleum Technology Conference A1 - Liu, C. A1 - Ghosh, D. A1 - Salim, A.M.A. A1 - Chow, W.S. TI - Fluid discrimination using bulk modulus and neural network SN - 9781613996195 PB - International Petroleum Technology Conference (IPTC) AV - none KW - Elastic moduli; Forecasting; Gas industry; Gasoline; Hydrocarbons; Lithology; Neural networks; Petroleum industry; Stochastic systems KW - Attributes; Hydrocarbon predictions; Learning approach; Oil and Gas Industry; Rock physical; Strong correlation KW - Deep learning UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126503814&doi=10.2523%2fiptc-19317-ms&partnerID=40&md5=6500bfb62cdc286b220334932ca35ac5 ID - scholars11847 ER -