eprintid: 9048
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/90/48
datestamp: 2023-11-09 16:20:59
lastmod: 2023-11-09 16:20:59
status_changed: 2023-11-09 16:14:08
type: conference_item
metadata_visibility: show
creators_name: Adeeyo, Y.A.
creators_name: Saaid, I.M.
title: Artificial neural network modelling of viscosity at bubblepoint pressure and dead oil viscosity of Nigerian Crude Oil
ispublished: pub
keywords: Backpropagation; Bioinformatics; Crude oil; Learning systems; Multilayer neural networks; Network layers; Petroleum reservoir evaluation; Predictive analytics, Artificial neural network modeling; Bubble point pressure; Coefficient of correlation; Computation algorithm; Levenberg-Marquardt algorithm; Optimization procedures; Reservoir engineering; Viscosity correlations, Viscosity
note: cited By 6; Conference of SPE Nigeria Annual International Conference and Exhibition 2017 ; Conference Date: 31 July 2017 Through 2 August 2017; Conference Code:133002
abstract: Reservoir engineering calculations required reservoir fluid viscosity data. In the absence of reliable experimental data, empirically derived correlations are used to predict the PVT property. However, several viscosity correlations in the literature were developed using regional data with limited accuracy and applicability. Artificial neural network (ANN) is a computer model that attempts to mimic a simple biological learning process and simulate specific functions of human nervous system, a new computation algorithm that could be used to develop more robust and reliable model for generating PVT data. It is an interconnection of nodes, called neurons. The authors have evaluated the industry widely used viscosity correlations using Nigeria crude oil data and explored the use of neural network in estimating viscosity at bubblepoint pressure and dead oil viscosity with the aim of getting a more accurate oil viscosity predictive model compared to widely used correlations in the literature. This study used the ANN backward propagation procedure with the Levenberg-Marquardt algorithm for the optimization procedure for both the viscosity at the bubblepoint pressure and dead oil viscosity models. The number of data set used for viscosity at the bubblepoint pressure is 1809 and dead oil viscosity is 1750. A number of neural network hidden layer designs were considered and tested. Each successful trained model was tested to ensure that overfitting does not occur and can predict output from the inputs that were not seen by the model during training. Sixty percent of the data was used to train the network, twenty percent to cross-validate the relationships established during the training process and the remaining twenty percent to test the model. The results of artificial neural network model for viscosity at bubblepoint show that the model gives higher accuracy compared to the published correlations with average absolute relative error of 11.05 and coefficient of correlation of 0.98. Besides, the dead oil viscosity neural network model shows a substantial improvement over correlations with average absolute relative error of 12.6 and coefficient of correlation of 0.91. Copyright 2017, Society of Petroleum Engineers.
date: 2017
publisher: Society of Petroleum Engineers
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050406398&doi=10.2118%2f189142-ms&partnerID=40&md5=1fb30dac3f5627d36b20a81693f5b21b
id_number: 10.2118/189142-ms
full_text_status: none
publication: Society of Petroleum Engineers - Nigeria Annual International Conference and Exhibition 2017
pagerange: 95-106
refereed: TRUE
isbn: 9781510846807
citation:   Adeeyo, Y.A. and Saaid, I.M.  (2017) Artificial neural network modelling of viscosity at bubblepoint pressure and dead oil viscosity of Nigerian Crude Oil.  In: UNSPECIFIED.