eprintid: 6123
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/61/23
datestamp: 2023-11-09 16:17:52
lastmod: 2023-11-09 16:17:52
status_changed: 2023-11-09 16:04:57
type: conference_item
metadata_visibility: show
creators_name: Mukhtar, S.M.
creators_name: Daud, H.
creators_name: Dass, S.C.
title: Prediction of Hydrocarbon Using Gaussian Process for Seabed Logging Application
ispublished: pub
keywords: Application programs; Computer software; Electromagnetic logging; Electromagnetic waves; Gaussian distribution; Gaussian noise (electronic); Hydrocarbons; Information systems; MATLAB; Seawater; Stochastic models; Stochastic systems, Computer simulation technology; Covariance; Covariance function; Gaussian Processes; High resistivity; Logging applications; Non-parametric model; Synthetic data, Oil well logging
note: cited By 7; Conference of 3rd Information Systems International Conference, 2015 ; Conference Date: 16 April 2015 Through 18 April 2015; Conference Code:123178
abstract: Seabed Logging (SBL) is a technique that utilizes electromagnetic waves to propagate signals underneath seabed to determine the differences in resistivity levels in order to determine possible oil wells for exploration. This research investigates the potential of a Gaussian process approach to identify the presence of potential hydrocarbon in the deep water environment. Simulations were conducted using Computer Simulation Technology software that replicates the real seabed logging applications to generate various synthetic data. Hydrocarbon is known to have high resistivity, about 30 - 500 ohm-meter if compared to sea water of 1 - 2 ohm-meter and sediment of 2 - 3 ohm-meter. From our simulations, we notice that the depth more than 1,750 m of offset the data is not reliable. Then, from the functions, we determine if it comes from the environment with hydrocarbon or without hydrocarbon. Data collected were processed using Gaussian Process method and focused on squared exponential covariance function types using codes in MATLAB. © 2015 The Authors.
date: 2015
publisher: Elsevier B.V.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84964026846&doi=10.1016%2fj.procs.2015.12.135&partnerID=40&md5=f7ec55c94105af51e8bba100a1141454
id_number: 10.1016/j.procs.2015.12.135
full_text_status: none
publication: Procedia Computer Science
volume: 72
pagerange: 225-232
refereed: TRUE
issn: 18770509
citation:   Mukhtar, S.M. and Daud, H. and Dass, S.C.  (2015) Prediction of Hydrocarbon Using Gaussian Process for Seabed Logging Application.  In: UNSPECIFIED.