Adaptive neuro fuzzy inference system for compressional wave velocity prediction in a carbonate reservoir

Compressional-wave (Vp) data are key information for estimation of rock physical properties and formation evaluation in hydrocarbon reservoirs. However, the absence of Vp will significantly delay the application of specific risk-assessment approaches for reservoir exploration and development procedu...

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Main Authors: Zoveidavianpoor, Mansoor, Samsuri, Ariffin, Shadizadeh, Seyed Reza
Format: Article
Published: 2013
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Online Access:http://eprints.utm.my/id/eprint/49740/
http://dx.doi.org/10.1016/j.jappgeo.2012.11.010
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Institution: Universiti Teknologi Malaysia
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spelling my.utm.497402018-10-14T08:26:37Z http://eprints.utm.my/id/eprint/49740/ Adaptive neuro fuzzy inference system for compressional wave velocity prediction in a carbonate reservoir Zoveidavianpoor, Mansoor Samsuri, Ariffin Shadizadeh, Seyed Reza TP Chemical technology Compressional-wave (Vp) data are key information for estimation of rock physical properties and formation evaluation in hydrocarbon reservoirs. However, the absence of Vp will significantly delay the application of specific risk-assessment approaches for reservoir exploration and development procedures. Since Vp is affected by several factors such as lithology, porosity, density, and etc., it is difficult to model their non-linear relationships using conventional approaches. In addition, currently available techniques are not efficient for Vp prediction, especially in carbonates. There is a growing interest in incorporating advanced technologies for an accurate prediction of lacking data in wells. The objectives of this study, therefore, are to analyze and predict Vp as a function of some conventional well logs by two approaches; Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multiple Linear Regression (MLR). Also, the significant impact of selected input parameters on response variable will be investigated. A total of 2156 data points from a giant Middle Eastern carbonate reservoir, derived from conventional well logs and Dipole Sonic Imager (DSI) log were utilized in this study. The quality of the prediction was quantified in terms of the mean squared error (MSE), correlation coefficient (R-square), and prediction efficiency error (PEE). Results show that the ANFIS outperforms MLR with MSE of 0.0552, R-square of 0.964, and PEE of 2%. It is posited that porosity has a significant impact in predicting Vp in the investigated carbonate reservoir 2013 Article PeerReviewed Zoveidavianpoor, Mansoor and Samsuri, Ariffin and Shadizadeh, Seyed Reza (2013) Adaptive neuro fuzzy inference system for compressional wave velocity prediction in a carbonate reservoir. Journal of Applied Geophysics, 89 . pp. 96-107. ISSN 0926-9851 http://dx.doi.org/10.1016/j.jappgeo.2012.11.010 DOI: 10.1016/j.jappgeo.2012.11.010
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TP Chemical technology
spellingShingle TP Chemical technology
Zoveidavianpoor, Mansoor
Samsuri, Ariffin
Shadizadeh, Seyed Reza
Adaptive neuro fuzzy inference system for compressional wave velocity prediction in a carbonate reservoir
description Compressional-wave (Vp) data are key information for estimation of rock physical properties and formation evaluation in hydrocarbon reservoirs. However, the absence of Vp will significantly delay the application of specific risk-assessment approaches for reservoir exploration and development procedures. Since Vp is affected by several factors such as lithology, porosity, density, and etc., it is difficult to model their non-linear relationships using conventional approaches. In addition, currently available techniques are not efficient for Vp prediction, especially in carbonates. There is a growing interest in incorporating advanced technologies for an accurate prediction of lacking data in wells. The objectives of this study, therefore, are to analyze and predict Vp as a function of some conventional well logs by two approaches; Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multiple Linear Regression (MLR). Also, the significant impact of selected input parameters on response variable will be investigated. A total of 2156 data points from a giant Middle Eastern carbonate reservoir, derived from conventional well logs and Dipole Sonic Imager (DSI) log were utilized in this study. The quality of the prediction was quantified in terms of the mean squared error (MSE), correlation coefficient (R-square), and prediction efficiency error (PEE). Results show that the ANFIS outperforms MLR with MSE of 0.0552, R-square of 0.964, and PEE of 2%. It is posited that porosity has a significant impact in predicting Vp in the investigated carbonate reservoir
format Article
author Zoveidavianpoor, Mansoor
Samsuri, Ariffin
Shadizadeh, Seyed Reza
author_facet Zoveidavianpoor, Mansoor
Samsuri, Ariffin
Shadizadeh, Seyed Reza
author_sort Zoveidavianpoor, Mansoor
title Adaptive neuro fuzzy inference system for compressional wave velocity prediction in a carbonate reservoir
title_short Adaptive neuro fuzzy inference system for compressional wave velocity prediction in a carbonate reservoir
title_full Adaptive neuro fuzzy inference system for compressional wave velocity prediction in a carbonate reservoir
title_fullStr Adaptive neuro fuzzy inference system for compressional wave velocity prediction in a carbonate reservoir
title_full_unstemmed Adaptive neuro fuzzy inference system for compressional wave velocity prediction in a carbonate reservoir
title_sort adaptive neuro fuzzy inference system for compressional wave velocity prediction in a carbonate reservoir
publishDate 2013
url http://eprints.utm.my/id/eprint/49740/
http://dx.doi.org/10.1016/j.jappgeo.2012.11.010
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