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Cited by 8 publications
(5 citation statements)
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“…Several areas of application included reservoir characterization (Artun and Mohaghegh 2011;Raeesi et al 2012;Alizadeh et al 2012), candidate well selection for hydraulic fracturing treatments (Mohaghegh et al 1996), well-placement/trajectory optimization (Centilmen et al 1999;Doraisamy et al 2000;Johnson and Rogers 2001;Guyaguler and Horne 2000;Yeten et al 2003;Gokcesu et al 2005;Mohaghegh et al 2006), screening and optimization of secondary/enhanced oil recovery processes (Ayala and Ertekin 2005;Patel et al 2005;Demiryurek et al 2008;Artun et al 2010Artun et al , 2012Parada and Ertekin 2012;Amirian et al 2013), history matching (Cullick et al 2006Silva et al 2007;Zhao et al 2015), reservoir modeling, monitoring and management (Zangl et al 2006;Mohaghegh 2011;Mohaghegh et al 2014;Zhao et al 2015;Kalantari-Dhaghi et al 2015;Esmaili and Mohaghegh 2016). Most of these problems presented in the literature are based on development of artificial neural network (ANN) based proxy models that can accurately mimic reservoir models within a reasonable amount of accuracy and computational efficiency.…”
Section: Data-driven Modeling Approach Using Artificial Neural Networkmentioning
confidence: 99%
“…Several areas of application included reservoir characterization (Artun and Mohaghegh 2011;Raeesi et al 2012;Alizadeh et al 2012), candidate well selection for hydraulic fracturing treatments (Mohaghegh et al 1996), well-placement/trajectory optimization (Centilmen et al 1999;Doraisamy et al 2000;Johnson and Rogers 2001;Guyaguler and Horne 2000;Yeten et al 2003;Gokcesu et al 2005;Mohaghegh et al 2006), screening and optimization of secondary/enhanced oil recovery processes (Ayala and Ertekin 2005;Patel et al 2005;Demiryurek et al 2008;Artun et al 2010Artun et al , 2012Parada and Ertekin 2012;Amirian et al 2013), history matching (Cullick et al 2006Silva et al 2007;Zhao et al 2015), reservoir modeling, monitoring and management (Zangl et al 2006;Mohaghegh 2011;Mohaghegh et al 2014;Zhao et al 2015;Kalantari-Dhaghi et al 2015;Esmaili and Mohaghegh 2016). Most of these problems presented in the literature are based on development of artificial neural network (ANN) based proxy models that can accurately mimic reservoir models within a reasonable amount of accuracy and computational efficiency.…”
Section: Data-driven Modeling Approach Using Artificial Neural Networkmentioning
confidence: 99%
“…Artificial neural networks (ANN) are very powerful in extracting non-linear and complex relationships between input and output patterns. Several areas of application included reservoir characterization (Artun and Mohaghegh 2011;Raeesi et al 2012;Alizadeh et al 2012;Artun 2016), candidate well selection for hydraulic fracturing treatments (Mohaghegh et al 1996), field development (Centilmen et al 1999;Doraisamy et al 2000;Mohaghegh et al 1996), well-placement and trajectory optimization Rogers 2011, Guyaguler 2002;Yeten et al 2003), scheduling of cyclic steam injection processes (Patel et al 2005), screening and optimization of secondary/enhanced oil recovery (Ayala and Ertekin 2005;Artun et al 2010Artun et al , 2011bArtun et al , 2012Parada and Ertekin 2012;Amirian et al 2013), history matching (Cullick et al 2006;Silva et al 2007;Zhao et al 2015), underground-gas-storage management (Zangl et al 2006), reservoir monitoring and management (Zhao et al 2015;Mohaghegh et al 2014), and modeling of shale-gas reservoirs (Kalantari-Dhaghi et al 2015;Esmaili and Mohaghegh 2015).…”
Section: Development Of a Screening Toolmentioning
confidence: 99%
“…In the 1990s and 2000s, nitrogen and mixtures with nitrogen were proposed and successfully applied (Shayegi et al 1996;Miller and Gaudin 2000). Artun et al (2010Artun et al ( , 2011aArtun et al ( , b, 2012 performed detailed parametric studies of the process by analyzing a large set of reservoir simulation runs and developed proxy models to be used for screening and optimization of cyclic pressure pulsing with nitrogen and carbon dioxide in naturally fractured reservoirs. These studies showed that cyclic pressure pulsing can be an effective enhanced oil recovery method in naturally fractured reservoirs.…”
Section: Introductionmentioning
confidence: 99%
“…In addition to their applications for unconventional reservoirs and secondary recovery, successful EOR forecasting and screening models have also been developed. Evaluations of different EOR methods, chemical flooding methods, cyclic pressure pulsing, , and thermal methods including SAGD , were made using data-driven models. A combination of models for different EOR processes can result in an comprehensive toolbox that would help to optimize the design of potential EOR applications …”
Section: Introductionmentioning
confidence: 99%