Predicting viscosity of CO2–N2 gaseous mixtures using advanced intelligent schemes

Naghizadeh, Arefeh; Larestani, Aydin; Amar, Menad Nait; Hemmati-Sarapardeh, Abdolhossein

doi:10.1016/j.petrol.2021.109359

Cited by 35 publications

(9 citation statements)

References 93 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to statistically evaluate the performance of the developed models, a variety of statistical indexes, namely, the root mean square error (rmse), average percent relative error (APRE), standard deviation (StD), AAPRE, and correlation coefficient ( R 2 ), were employed. The equations of the considered errors are provided here 24 , 49 …”

Section: Results and Discussionmentioning

confidence: 99%

Compositional Modeling of the Oil Formation Volume Factor of Crude Oil Systems: Application of Intelligent Models and Equations of State

et al. 2022

Self Cite

View full text Add to dashboard Cite

This communication primarily concentrates on developing reliable and accurate compositional oil formation volume factor ( B o ) models using several advanced and powerful machine learning (ML) models, namely, extra trees (ETs), random forest (RF), decision trees (DTs), generalized regression neural networks, and cascade-forward back-propagation network, alongside radial basis function and multilayer perceptron neural networks. Along with these models, seven equations of state (EoSs) were employed to estimate B o . The performance of the developed ML models and employed EoSs was assessed through various statistical and graphical evaluations. Overall, the ML models could provide much more accurate predictions in comparison to EoSs. However, the results indicated that tree-based models, specifically ET models, could outperform the other models and can be reliably applied for estimating B o . The most reliable ET model could predict B o with a total average error of 1.17%. Lastly, the outlier detection approach verified the dataset’s consistency detecting only 17 (out of 1224) data points as outliers for the proposed B o models.

show abstract

Section: Results and Discussionmentioning

confidence: 99%

Compositional Modeling of the Oil Formation Volume Factor of Crude Oil Systems: Application of Intelligent Models and Equations of State

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…The zero or close value to zero implies no relationship between a pair of variables. It is possible to calculate the Pearson's coefficient using Equation () 74

{r}_{xy}=\sum _{k=1}^{N}({x}_{k}-\bar{x})({y}_{k}-\bar{y})/\left(\sqrt{{\sum }_{k=1}^{N}{({x}_{k}-\bar{x})}^{2}}\sqrt{{\sum }_{k=1}^{N}{({y}_{k}-\bar{y})}^{2}}\right),

here,

\bar{x}

(Equation ) and

\bar{y}

(Equation ) are average values of x and y variables, respectively.…”

Section: Discussionmentioning

confidence: 99%

Determination of the heat capacity of cellulosic biosamples employing diverse machine learning approaches

Karimi

Khosravi

Fathollahi

et al. 2022

Energy Science & Engineering

View full text Add to dashboard Cite

Heat capacity is among the most well‐known thermal properties of cellulosic biomass samples. This study assembles a general machine learning model to estimate the heat capacity of the cellulosic biomass samples with different origins. Combining the uncertainty and ranking analyses over 819 artificial intelligence models from seven different categories confirmed that the least‐squares support vector regression (LSSVR) with the Gaussian kernel function is the best estimator. This model is validated using 700 laboratory heat capacities of four cellulosic biomass samples in wide temperature ranges (absolute average relative deviation = 0.32%, mean square errors = 1.88 × 10−3, and R2 = 0.999991). The data validity investigation approved that only one out of 700 experimental data is an outlier. The LSSVR model considers the effect of the cellulosic samples' crystallinity, temperature, and sulfur and ash content on their heat capacity. The overall prediction accuracy of the LSSVR is more than 62% better than the achieved accuracy using the empirical correlation.

show abstract

“…In order to tune the smart techniques, a databank containing 3036 data points was utilized, with input parameters including pressure, temperature, and mole fraction of CO 2 from previous studies. ,,− This data bank was also used by Naghizadeh et al Table summarizes the statistical properties of the dataset. Throughout the development process, the entire dataset was divided randomly into two subsets, namely, train and test parts carrying 80 and 20% of all data points, respectively.…”

Section: Development Of Intelligent Modelsmentioning

confidence: 99%

“…AlQuraishi and Shokir 51 implemented generalized regression neural networks (GRNNs) on 4445 experimental measurements containing pure gases and gas mixtures to develop a prediction model for viscosity by an AAPRE of 3.65%. In a recent study, Naghizadeh et al 52 developed a predictive model to estimate CO 2 –N 2 viscosities using multilayer perceptron (MLP), boosted regression tree (BRT) coupled with evolutionary algorithms, cascade forward neural network (CFNN), and GRNN smart paradigms. The results of their work yielded RMSE and R 2 values of 3.95 and 0.9975, respectively, for the BRT network coupled with an artificial bee colony (ABC) optimizer in the testing data set.…”

Section: Introductionmentioning

confidence: 99%

Modeling Viscosity of CO₂–N₂ Gaseous Mixtures Using Robust Tree-Based Techniques: Extra Tree, Random Forest, GBoost, and LightGBM

Zheng¹,

Mahmoudzadeh

Amiri-Ramsheh

et al. 2023

ACS Omega

Self Cite

View full text Add to dashboard Cite

Carbon dioxide (CO 2 ) has an essential role in most enhanced oil recovery (EOR) methods in the oil industry. Oil swelling and viscosity reduction are the dominant mechanisms in an immiscible CO 2 -EOR process. Besides numerous CO 2 applications in EOR, most oil reservoirs do not have access to natural CO 2 , and capturing it from flue gas and other sources is costly. Flue gases are available in huge quantities at a significantly lower price and can be considered economically viable agents for EOR operations. In this work, four powerful machine learning algorithms, namely, extra tree (ET), random forest (RF), gradient boosting (GBoost), and light gradient boosted machine (LightGBM) were utilized to accurately estimate the viscosity of CO 2 −N 2 mixtures. To this aim, a databank was employed, containing 3036 data points over wide ranges of pressures and temperatures. Temperature, pressure, and CO 2 mole fraction were applied as input parameters, and the viscosity of the CO 2 −N 2 mixture was the output. The RF smart model had the highest precision with the lowest average absolute percent relative error (AAPRE) of 1.58%, root mean square error (RMSE) of 2.221, and determination coefficient (R 2 ) of 0.9993. The trend analysis showed that the RF model could precisely predict the real physical behavior of the CO 2 −N 2 viscosity variation. Finally, the outlier detection was performed using the leverage approach to demonstrate the validity of the utilized databank and the applicability area of the developed RF model. Accordingly, nearly 96% of the data points seemed to be dependable and valid, and the rest of them were located in the suspected and out-of-leverage data zones. 22

show abstract

Predicting viscosity of CO2–N2 gaseous mixtures using advanced intelligent schemes

Cited by 35 publications

References 93 publications

Compositional Modeling of the Oil Formation Volume Factor of Crude Oil Systems: Application of Intelligent Models and Equations of State

Compositional Modeling of the Oil Formation Volume Factor of Crude Oil Systems: Application of Intelligent Models and Equations of State

Determination of the heat capacity of cellulosic biosamples employing diverse machine learning approaches

Modeling Viscosity of CO₂–N₂ Gaseous Mixtures Using Robust Tree-Based Techniques: Extra Tree, Random Forest, GBoost, and LightGBM

Contact Info

Product

Resources

About

Predicting viscosity of CO2–N2 gaseous mixtures using advanced intelligent schemes

Cited by 35 publications

References 93 publications

Compositional Modeling of the Oil Formation Volume Factor of Crude Oil Systems: Application of Intelligent Models and Equations of State

Compositional Modeling of the Oil Formation Volume Factor of Crude Oil Systems: Application of Intelligent Models and Equations of State

Determination of the heat capacity of cellulosic biosamples employing diverse machine learning approaches

Modeling Viscosity of CO2–N2 Gaseous Mixtures Using Robust Tree-Based Techniques: Extra Tree, Random Forest, GBoost, and LightGBM

Contact Info

Product

Resources

About

Modeling Viscosity of CO₂–N₂ Gaseous Mixtures Using Robust Tree-Based Techniques: Extra Tree, Random Forest, GBoost, and LightGBM