Reservoir rock properties estimation based on conventional and NMR log data using ANN-Cuckoo: A case study in one of super fields in Iran southwest

This work highlights the application of Artificial Neural Networks optimized by Cuckoo optimization algorithm for predictions of NMR log parameters including porosity and permeability by using field log data. The NMR logging data have some highly vital privileges over conventional ones. The measured...

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Main Authors: Ghasem Zargar, Abbas Ayatizadeh Tanha, Amirhossein Parizad, Mehdi Amouri, Hasan Bagheri
Format: Article
Language:English
Published: KeAi Communications Co., Ltd. 2020-09-01
Series:Petroleum
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2405656119300598
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spelling doaj-3b17225d7d494337b007e0239bdcfc5b2021-04-02T16:36:58ZengKeAi Communications Co., Ltd.Petroleum2405-65612020-09-0163304310Reservoir rock properties estimation based on conventional and NMR log data using ANN-Cuckoo: A case study in one of super fields in Iran southwestGhasem Zargar0Abbas Ayatizadeh Tanha1Amirhossein Parizad2Mehdi Amouri3Hasan Bagheri4Petroleum University of Technology, Iran; Corresponding author. Petroleum University of Technology, Iran.Petroleum University of Technology, Iran; National Iranian Drilling Company, Well Logging Department, Ahwaz, IranPetroleum University of Technology, Iran; Petro Gostaran Ofogh (PGO), Mud Logging Department, Ahwaz, Iran; Corresponding author. Petroleum University of Technology, Iran.National Iranian Drilling Company, Well Logging Department, Ahwaz, IranNational Iranian Drilling Company, Well Logging Department, Ahwaz, IranThis work highlights the application of Artificial Neural Networks optimized by Cuckoo optimization algorithm for predictions of NMR log parameters including porosity and permeability by using field log data. The NMR logging data have some highly vital privileges over conventional ones. The measured porosity is independent from bearer pore fluid and is effective porosity not total. Moreover, the permeability achieved by exact measurement and calculation considering clay content and pore fluid type. Therefore availability of the NMR data brings a great leverage in understanding the reservoir properties and also perfectly modelling the reservoir. Therefore, achieving NMR logging data by a model fed by a far inferior and less costly conventional logging data is a great privilege. The input parameters of model were neutron porosity (NPHI), sonic transit time (DT), bulk density (RHOB) and electrical resistivity (RT). The outputs of model were also permeability and porosity values. The structure developed model was build and trained by using train data. Graphical and statistical validation of results showed that the developed model is effective in prediction of field NMR log data. Outcomes show great possibility of using conventional logging data be used in order to reach the precious NMR logging data without any unnecessary costly tests for a reservoir. Moreover, the considerable accuracy of newly ANN-Cuckoo method also demonstrated. This study can be an illuminator in areas of reservoir engineering and modelling studies were presence of accurate data must be essential.http://www.sciencedirect.com/science/article/pii/S2405656119300598Neural networkANN-CuckooNMR loggingPermeability modelingPorosity modeling
collection DOAJ
language English
format Article
sources DOAJ
author Ghasem Zargar
Abbas Ayatizadeh Tanha
Amirhossein Parizad
Mehdi Amouri
Hasan Bagheri
spellingShingle Ghasem Zargar
Abbas Ayatizadeh Tanha
Amirhossein Parizad
Mehdi Amouri
Hasan Bagheri
Reservoir rock properties estimation based on conventional and NMR log data using ANN-Cuckoo: A case study in one of super fields in Iran southwest
Petroleum
Neural network
ANN-Cuckoo
NMR logging
Permeability modeling
Porosity modeling
author_facet Ghasem Zargar
Abbas Ayatizadeh Tanha
Amirhossein Parizad
Mehdi Amouri
Hasan Bagheri
author_sort Ghasem Zargar
title Reservoir rock properties estimation based on conventional and NMR log data using ANN-Cuckoo: A case study in one of super fields in Iran southwest
title_short Reservoir rock properties estimation based on conventional and NMR log data using ANN-Cuckoo: A case study in one of super fields in Iran southwest
title_full Reservoir rock properties estimation based on conventional and NMR log data using ANN-Cuckoo: A case study in one of super fields in Iran southwest
title_fullStr Reservoir rock properties estimation based on conventional and NMR log data using ANN-Cuckoo: A case study in one of super fields in Iran southwest
title_full_unstemmed Reservoir rock properties estimation based on conventional and NMR log data using ANN-Cuckoo: A case study in one of super fields in Iran southwest
title_sort reservoir rock properties estimation based on conventional and nmr log data using ann-cuckoo: a case study in one of super fields in iran southwest
publisher KeAi Communications Co., Ltd.
series Petroleum
issn 2405-6561
publishDate 2020-09-01
description This work highlights the application of Artificial Neural Networks optimized by Cuckoo optimization algorithm for predictions of NMR log parameters including porosity and permeability by using field log data. The NMR logging data have some highly vital privileges over conventional ones. The measured porosity is independent from bearer pore fluid and is effective porosity not total. Moreover, the permeability achieved by exact measurement and calculation considering clay content and pore fluid type. Therefore availability of the NMR data brings a great leverage in understanding the reservoir properties and also perfectly modelling the reservoir. Therefore, achieving NMR logging data by a model fed by a far inferior and less costly conventional logging data is a great privilege. The input parameters of model were neutron porosity (NPHI), sonic transit time (DT), bulk density (RHOB) and electrical resistivity (RT). The outputs of model were also permeability and porosity values. The structure developed model was build and trained by using train data. Graphical and statistical validation of results showed that the developed model is effective in prediction of field NMR log data. Outcomes show great possibility of using conventional logging data be used in order to reach the precious NMR logging data without any unnecessary costly tests for a reservoir. Moreover, the considerable accuracy of newly ANN-Cuckoo method also demonstrated. This study can be an illuminator in areas of reservoir engineering and modelling studies were presence of accurate data must be essential.
topic Neural network
ANN-Cuckoo
NMR logging
Permeability modeling
Porosity modeling
url http://www.sciencedirect.com/science/article/pii/S2405656119300598
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