%0 Conference Paper
%A Aris, M.N.M.
%A Daud, H.
%A Dass, S.C.
%D 2018
%F scholars:9489
%I Institute of Physics Publishing
%K Computer software; Electric conductivity; Electromagnetic logging; Forecasting; Gaussian distribution; Gaussian noise (electronic); Offshore oil well production, Computer simulation technology (CST); Controlled source electromagnetic (CSEM); Hydrocarbon reservoir; Mathematical equations; Offshore environments; Offshore hydrocarbons; Supervised learning methods; Time-consuming tasks, Hydrocarbons
%N 1
%R 10.1088/1742-6596/1132/1/012075
%T Prediction of hydrocarbon depth for seabed logging (SBL) application using Gaussian process
%U https://khub.utp.edu.my/scholars/9489/
%V 1132
%X Seabed Logging (SBL) is an application of marine Controlled-Source Electromagnetic (CSEM) technique to characterize hydrocarbon-filled layers underneath the seabed remotely in deep water regions. This technique maps structure of subsurface electrical resistivity in the offshore environment. Basically, exploration of offshore hydrocarbon is based on the contrast of electrical resistivity between hydrocarbon reservoir and its surrounding sea sediments. Modelling offshore hydrocarbon is a core analysis and time consuming task. Current numerical modelling techniques used in SBL application involve meshes and complicated mathematical equations. Thus, a simple supervised learning method which is Gaussian Process (GP) is proposed to process synthetic SBL data which are generated through Computer Simulation Technology (CST) software to predict the depth of hydrocarbon. This statistical model is able to provide additional hydrocarbon information by utilizing the prior information. 1-dimensional (1-D) forward GP models have successfully been developed to predict the presence of hydrocarbon and as the continuation work, 2-dimensional (2-D) forward GP model is then developed to be used as the standard profile in order to predict the depth of hydrocarbon. This shall give indication that GP can be used as the methodology to predict the depth of hydrocarbon reservoir underneath the seabed. © Published under licence by IOP Publishing Ltd.
%Z cited By 2; Conference of 3rd International Conference on Mathematical Sciences and Statistics, ICMSS 2018 ; Conference Date: 6 February 2018 Through 8 February 2018; Conference Code:143023