PLC selection

Fluid Phase Equilibria 337 (2013) 89–99

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Fluid Phase Equilibria journal homepage: www.elsevier.com/locate/fluid

Asphaltene deposition prediction using adaptive neuro-fuzzy models based on laboratory measurements
Karim Salahshoor a , Sepide Zakeri a , Sedigheh Mahdavi b,∗ , Riyaz Kharrat a , Mahmoud Khalifeh b a b

Petroleum University of Technology, Tehran, Iran
Petroleum Research Center, Petroleum University of Technology, Tehran, Iran

a r t i c l e

i n f o

Article history:
Received 21 April 2012
Received in revised form 25 August 2012
Accepted 24 September 2012
Available online 2 October 2012
Keywords:
Adaptive neuro-fuzzy model
Affine model
Asphaltene deposition
Permeability
Pressure drop

a b s t r a c t
Deposition of asphaltene is recognized as a well-known severe problem, which can significantly affect oil production and enhanced oil recovery processes through mechanism of wettability alteration and blockage. The natural mechanism is not fully comprehended until now due to impossibility to carry out actual field experiments. In this work, different flow dynamic test scenarios are organized to perform on sandstone as well as carbonate rocks to practically explore process of asphaltene deposition. Ordinary optimized methods are not applicable to asphaltene deposition due to its dependency on the involved parameters and complexity of process. The permeability impairment data is monitored through analysis of recorded pressures during the test experiments. Then, a new adaptive neuro-fuzzy inference system is developed to predict asphaltene deposition in terms of permeability (K/K0 ) and pressure drop (DP), considering pore volume injection (PVI) and time data as input variables. Accordingly, two adaptive neuro-fuzzy models are sequentially developed in a nonlinear affine-type configuration to investigate the effect of multiple variables and parameters on asphaltene

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