eprintid: 18822
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/88/22
datestamp: 2024-06-04 14:11:14
lastmod: 2024-06-04 14:11:14
status_changed: 2024-06-04 14:04:10
type: article
metadata_visibility: show
creators_name: Sikiru, Surajudeen
creators_name: Soleimani, H.
creators_name: Shafie, A.
creators_name: Olayemi, R.I.
creators_name: Hassan, Y.M.
title: Prediction of Electromagnetic Properties Using Artificial Neural Networks for Oil Recovery Factors
ispublished: pub
keywords: Computer system recovery; Enhanced recovery; Forecasting; Gas industry; Multilayer neural networks; Petroleum reservoir engineering; Recovery; Secondary recovery, Electromagnetic properties; Enhanced-oil recoveries; Oil and Gas Industry; Oil consumption; Oil Prices; Oil recoveries; Operational conditions; Original oil in places; Recovery factors; Water saturations, Deep neural networks
note: cited By 0
abstract: Abstract: The most important parameter in the oil and gas industry is the recovery factor (RF). Higher oil consumption has resulted in a rise in global oil prices. Several research have been carried out in order to create, analyze, and optimize operational conditions. However, several reservoir factors such as viscosity, porosity, permeability, water saturation, original oil-in-place, and API gravity have been employed in the development of RF to boost oil recovery techniques without taking electromagnetic properties into account. The recovery factor was predicted using core flooding tests and a deep neural network using electromagnetic parameters. We offer a deep neural network (DNN) method with 256 nodes, seven hidden layers, and a single output. According to the acquired results, the DNN algorithms' coefficient correlation is R2 = 0.98478 for training and R2 = 0.91679 for testing, which was subsequently evaluated and confirmed for RF prediction. In terms of cost and production effectiveness, the results of this study reveal a good forecast of RF with reservoir rock and fluid parameters. © 2023, Pleiades Publishing, Ltd.
date: 2023
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85152886016&doi=10.1134%2fS1061933X2260004X&partnerID=40&md5=07ad91d8a631d8136b03b2e262b63521
id_number: 10.1134/S1061933X2260004X
full_text_status: none
publication: Colloid Journal
volume: 85
number: 1
pagerange: 151-165
refereed: TRUE
citation:   Sikiru, Surajudeen and Soleimani, H. and Shafie, A. and Olayemi, R.I. and Hassan, Y.M.  (2023) Prediction of Electromagnetic Properties Using Artificial Neural Networks for Oil Recovery Factors.  Colloid Journal, 85 (1).  pp. 151-165.