relation: https://khub.utp.edu.my/scholars/11847/
title: Fluid discrimination using bulk modulus and neural network
creator: Liu, C.
creator: Ghosh, D.
creator: Salim, A.M.A.
creator: Chow, W.S.
description: 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
publisher: International Petroleum Technology Conference (IPTC)
date: 2019
type: Conference or Workshop Item
type: PeerReviewed
identifier:   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.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126503814&doi=10.2523%2fiptc-19317-ms&partnerID=40&md5=6500bfb62cdc286b220334932ca35ac5
relation: 10.2523/iptc-19317-ms
identifier: 10.2523/iptc-19317-ms