Estimation of Viscosity of the N-Alkane (C1-C 4) in Bitumen System Using Adaptive Neuro-Fuzzy Interference System (ANFIS)

2020 ◽  
Vol 4 (3) ◽  
pp. 1-5
Author(s):  
Kuyakhi HR
Keyword(s):  
Thermal Recovery ◽  
Viscosity Reduction ◽  
Graphical Methods ◽  
Statistical Parameters ◽  
Anfis Model ◽  
Recovery Processes ◽  
Fuzzy Interference System ◽  
Interference System ◽  
Neuro Fuzzy ◽  
Mixture Viscosity

One of the important mechanisms in solvent-aided thermal recovery processes is viscosity reduction. Light n-alkane hydrocarbons are among the potential solvents for solvent-aided thermal recovery processes. In this study, the viscosity of C1- C4 n-alkanes in bitumen was investigated. Adaptive neuro-fuzzy interference system (ANFIS) was used for this aim. The result obtained by the ANFIS model analyzed with the statistical parameters (i.e., MSE, MEAE, MAAE, and R2) and graphical methods. Results show that the ANFIS has high capability to the prediction of solvent/bitumen mixture viscosity.

scholarly journals Estimation of maximum scour depth downstream of an apron under submerged wall jets

2019 ◽  
Vol 21 (4) ◽  
pp. 523-540 ◽  
Author(s):  
Mohammad Aamir ◽  
Zulfequar Ahmad
Keyword(s):  
Scour Depth ◽  
Regression Equations ◽  
Wall Jets ◽  
Statistical Parameters ◽  
Densimetric Froude Number ◽  
Sediment Size ◽  
Fuzzy Interference System ◽  
Interference System ◽  
Neuro Fuzzy ◽  
Artificial Neural Network Ann

Abstract An analysis of laboratory experimental data pertaining to local scour downstream of a rigid apron developed under wall jets is presented. The existing equations for the prediction of the maximum scour depth under wall jets are applied to the available data to evaluate their performance and bring forth their limitations. A comparison of measured scour depth with that computed by the existing equations shows that most of the existing empirical equations perform poorly. Artificial neural network (ANN)- and adaptive neuro-fuzzy interference system (ANFIS)-based models are developed using the available data, which provide simple and accurate tools for the estimation of the maximum scour depth. The key parameters that affect the maximum scour depth are densimetric Froude number, apron length, tailwater level, and median sediment size. Results obtained from ANN and ANFIS models are compared with those of empirical and regression equations by means of statistical parameters. The performance of ANN (RMSE = 0.052) and ANFIS (RMSE = 0.066) models is more satisfactory than that of empirical and regression equations.

Nonlinear Double-Log Mixing Rule for Viscosity Calculation of Bitumen/Solvent Mixtures Applicable for Reservoir Simulation of Solvent-Based Recovery Processes

SPE Journal ◽  
2020 ◽  
Vol 25 (05) ◽  
pp. 2648-2662
Author(s):  
Hossein Nourozieh ◽  
Ehsan Ranjbar ◽  
Anjani Kumar ◽  
Kevin Forrester ◽  
Mohsen Sadeghi
Keyword(s):  
Reservoir Simulation ◽  
Heavy Oil ◽  
Viscosity Reduction ◽  
Viscosity Data ◽  
Solvent Mixtures ◽  
Mixing Rule ◽  
Arrhenius Model ◽  
Recovery Processes ◽  
Mixture Phase ◽  
Mixture Viscosity

Summary Various solvent-based recovery processes for bitumen and heavy-oil reservoirs have gained much interest in recent years. In these processes, viscosity reduction is attained not only because of thermal effects, but also by dilution of bitumen with a solvent. Accurate characterization of the oil/solvent-mixture viscosity is critical for accurate prediction of recovery and effectiveness of such processes. There are varieties of models designed to predict and correlate the mixture viscosities. Among them, the linear log mixing (Arrhenius) model is the most commonly used method in the oil industry. This model, originally proposed for light oils, often show poor performance (40 to 60% error) when applied to highly viscous fluids such as heavy oil and bitumen. The modified Arrhenius model, called the nonlinear log mixing model, gives slightly better predictions compared with the original Arrhenius model. However, the predictions still might not be acceptable because of large deviations from measured experimental data. Calculated mixture-phase viscosity has a significant effect on flow calculations in commercial reservoir simulators. Underestimation of mixture viscosities leads to overprediction of oil-production rates. Using such mixing models in reservoir simulation can lead to inaccuracy in mixture viscosities and hence large uncertainty in model results. In the present study, different correlations and mixing rules available in the literature are evaluated against the mixture-viscosity data for a variety of bitumen/solvent systems. A new form (nonlinear) of the double-log mixing rule is proposed, which shows a significant improvement over the existing models on predicting viscosities of bitumen/solvent mixtures, especially at high temperatures.

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