eprintid: 15403
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/54/03
datestamp: 2023-11-10 03:30:01
lastmod: 2023-11-10 03:30:01
status_changed: 2023-11-10 01:59:24
type: conference_item
metadata_visibility: show
creators_name: Baarimah, S.O.
creators_name: Baarimah, A.O.
title: PVT Properties for Yemeni Reservoirs Using an Intelligent Approach
ispublished: pub
keywords: Computer circuits; Solubility; Viscosity, Bottomhole; Data set; Formation volume factors; Gas solubility; Intelligent approach; Oil viscosity; Property; PVT; PVT properties; Yemeni reservoir, Fuzzy logic
note: cited By 2; Conference of 3rd International Sustainability and Resilience Conference: Climate Change, ISRC 2021 ; Conference Date: 15 November 2021 Through 17 November 2021; Conference Code:176395
abstract: PVT empirical correlations and Artificial Intelligence (AI) techniques become the best alternative when laboratory PVT analysis is not ready or very expensive to obtain. The objective of this paper is to determine the most frequently used oil viscosity (μo), formation volume factor (βo), and gas solubility (Rs) PVT properties of Yemeni reservoirs using the bottom hole fluid samples from different wells such as Well-BSWS-1, Well-BSWS-2, Well-BSWS-3, and Well-BSWS-4. Both Fuzzy Logic (FL) technique and a set of statistical error analysis were used to validate and compare the performance and accuracy of the generated reservoir fluid properties correlations. A total of 200 data sets of different crude oils from Yemeni reservoirs were used. The accuracy of the new Fuzzy Logic (FL) was compared with existing real measured bottom hole fluid samples data sets. The graphical plots showed that the predicted oil viscosity, formation volume factor, and gas solubility Fuzzy Logic curves have excellent matching with the experimental curves. © 2021 IEEE.
date: 2021
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125091614&doi=10.1109%2fIEEECONF53624.2021.9668185&partnerID=40&md5=7fd2a30f9e29746e3f2619ccf3928557
id_number: 10.1109/IEEECONF53624.2021.9668185
full_text_status: none
publication: 2021 3rd International Sustainability and Resilience Conference: Climate Change
pagerange: 368-372
refereed: TRUE
isbn: 9781665416320
citation:   Baarimah, S.O. and Baarimah, A.O.  (2021) PVT Properties for Yemeni Reservoirs Using an Intelligent Approach.  In: UNSPECIFIED.