%0 Conference Paper
%A Liu, C.
%A Ghosh, D.
%A Salim, A.M.A.
%A Chow, W.S.
%D 2019
%F scholars:11847
%I International Petroleum Technology Conference (IPTC)
%K 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
%R 10.2523/iptc-19317-ms
%T Fluid discrimination using bulk modulus and neural network
%U https://khub.utp.edu.my/scholars/11847/
%X 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 �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 �K, and the gas can be detected with acceptable accuracy. Furthermore, a model using deep learning approach is trained to predict the �K. The trained model is effective that the predicted values using this model have a strong correlation with the original �K. The �K can be applied to the data which contains Vp, Vs and density using this approach model. In this study, the �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 �K improves our ability in hydrocarbon prediction. © 2019, International Petroleum Technology Conference
%Z cited By 1; Conference of International Petroleum Technology Conference 2019, IPTC 2019 ; Conference Date: 26 March 2019 Through 28 March 2019; Conference Code:146421