eprintid: 11847 rev_number: 2 eprint_status: archive userid: 1 dir: disk0/00/01/18/47 datestamp: 2023-11-10 03:26:23 lastmod: 2023-11-10 03:26:23 status_changed: 2023-11-10 01:16:17 type: conference_item metadata_visibility: show creators_name: Liu, C. creators_name: Ghosh, D. creators_name: Salim, A.M.A. creators_name: Chow, W.S. title: Fluid discrimination using bulk modulus and neural network ispublished: pub 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 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 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 �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 date: 2019 publisher: International Petroleum Technology Conference (IPTC) official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126503814&doi=10.2523%2fiptc-19317-ms&partnerID=40&md5=6500bfb62cdc286b220334932ca35ac5 id_number: 10.2523/iptc-19317-ms full_text_status: none publication: International Petroleum Technology Conference 2019, IPTC 2019 refereed: TRUE isbn: 9781613996195 citation: Liu, C. and Ghosh, D. and Salim, A.M.A. and Chow, W.S. (2019) Fluid discrimination using bulk modulus and neural network. In: UNSPECIFIED.