Application of cascade forward neural network and group method of data handling to modeling crude oil pyrolysis during thermal enhanced oil recovery

Mohammadi, Mohammad-Reza; Hemmati-Sarapardeh, Abdolhossein; Schaffie, Mahin; Husein, Maen M.; Ranjbar, Mohammad

doi:10.1016/j.petrol.2021.108836

Cited by 61 publications

(20 citation statements)

References 62 publications

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“…However, for r < 0, input has a reverse impact on output; in other words, an increasing trend in target value comes from decreasing independent parameters. Furthermore, how much the absolute value of r is close to 1, there is a stronger relationship between the two parameters …”

Section: Resultsmentioning

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

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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

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Section: Resultsmentioning

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

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“…As can be seen in Figure 12, the majority of the data are located in the interval of −3 ≤ R ≤ 3 and 0 ≤ H ≤ H*. The points with lower values of R and H are more reliable [70,71]. Only six data points were recognized to be outside of the scope of the model applicability, which proves the high reliability of the RBF model for estimating the IFT of the N 2 /CO 2 mixture + n-alkanes.…”

Section: Model Reliability Assessment and Outlier Diagnosticsmentioning

confidence: 86%

Modeling Interfacial Tension of N2/CO2 Mixture + n-Alkanes with Machine Learning Methods: Application to EOR in Conventional and Unconventional Reservoirs by Flue Gas Injection

et al. 2022

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The combustion of fossil fuels from the input of oil refineries, power plants, and the venting or flaring of produced gases in oil fields leads to greenhouse gas emissions. Economic usage of greenhouse and flue gases in conventional and unconventional reservoirs would not only enhance the oil and gas recovery but also offers CO2 sequestration. In this regard, the accurate estimation of the interfacial tension (IFT) between the injected gases and the crude oils is crucial for the successful execution of injection scenarios in enhanced oil recovery (EOR) operations. In this paper, the IFT between a CO2/N2 mixture and n-alkanes at different pressures and temperatures is investigated by utilizing machine learning (ML) methods. To this end, a data set containing 268 IFT data was gathered from the literature. Pressure, temperature, the carbon number of n-alkanes, and the mole fraction of N2 were selected as the input parameters. Then, six well-known ML methods (radial basis function (RBF), the adaptive neuro-fuzzy inference system (ANFIS), the least square support vector machine (LSSVM), random forest (RF), multilayer perceptron (MLP), and extremely randomized tree (extra-tree)) were used along with four optimization methods (colliding bodies optimization (CBO), particle swarm optimization (PSO), the Levenberg–Marquardt (LM) algorithm, and coupled simulated annealing (CSA)) to model the IFT of the CO2/N2 mixture and n-alkanes. The RBF model predicted all the IFT values with exceptional precision with an average absolute relative error of 0.77%, and also outperformed all other models in this paper and available in the literature. Furthermore, it was found that the pressure and the carbon number of n-alkanes would show the highest influence on the IFT of the CO2/N2 and n-alkanes, based on sensitivity analysis. Finally, the utilized IFT database and the area of the RBF model applicability were investigated via the leverage method.

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“…To amend the limitations of the primary GMDH method 89 , the hybrid GMDH is usually utilized which has more than two independent variables that can be combined concurrently and it permits the intersection of nodal within diverse layers. The succeeding formula shows the final form of the hybrid GMDH 95 : …”

Section: Model Developmentmentioning

confidence: 99%

Modeling the solubility of light hydrocarbon gases and their mixture in brine with machine learning and equations of state

Mohammadi

Hadavimoghaddam

Atashrouz

et al. 2022

Sci Rep

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Knowledge of the solubilities of hydrocarbon components of natural gas in pure water and aqueous electrolyte solutions is important in terms of engineering designs and environmental aspects. In the current work, six machine-learning algorithms, namely Random Forest, Extra Tree, adaptive boosting support vector regression (AdaBoost-SVR), Decision Tree, group method of data handling (GMDH), and genetic programming (GP) were proposed for estimating the solubility of pure and mixture of methane, ethane, propane, and n-butane gases in pure water and aqueous electrolyte systems. To this end, a huge database of hydrocarbon gases solubility (1836 experimental data points) was prepared over extensive ranges of operating temperature (273–637 K) and pressure (0.051–113.27 MPa). Two different approaches including eight and five inputs were adopted for modeling. Moreover, three famous equations of state (EOSs), namely Peng-Robinson (PR), Valderrama modification of the Patel–Teja (VPT), and Soave–Redlich–Kwong (SRK) were used in comparison with machine-learning models. The AdaBoost-SVR models developed with eight and five inputs outperform the other models proposed in this study, EOSs, and available intelligence models in predicting the solubility of mixtures or/and pure hydrocarbon gases in pure water and aqueous electrolyte systems up to high-pressure and high-temperature conditions having average absolute relative error values of 10.65% and 12.02%, respectively, along with determination coefficient of 0.9999. Among the EOSs, VPT, SRK, and PR were ranked in terms of good predictions, respectively. Also, the two mathematical correlations developed with GP and GMDH had satisfactory results and can provide accurate and quick estimates. According to sensitivity analysis, the temperature and pressure had the greatest effect on hydrocarbon gases’ solubility. Additionally, increasing the ionic strength of the solution and the pseudo-critical temperature of the gas mixture decreases the solubilities of hydrocarbon gases in aqueous electrolyte systems. Eventually, the Leverage approach has revealed the validity of the hydrocarbon solubility databank and the high credit of the AdaBoost-SVR models in estimating the solubilities of hydrocarbon gases in aqueous solutions.

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Application of cascade forward neural network and group method of data handling to modeling crude oil pyrolysis during thermal enhanced oil recovery

Cited by 61 publications

References 62 publications

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

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

Modeling Interfacial Tension of N2/CO2 Mixture + n-Alkanes with Machine Learning Methods: Application to EOR in Conventional and Unconventional Reservoirs by Flue Gas Injection

Modeling the solubility of light hydrocarbon gases and their mixture in brine with machine learning and equations of state

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Application of cascade forward neural network and group method of data handling to modeling crude oil pyrolysis during thermal enhanced oil recovery

Cited by 61 publications

References 62 publications

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

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

Modeling Interfacial Tension of N2/CO2 Mixture + n-Alkanes with Machine Learning Methods: Application to EOR in Conventional and Unconventional Reservoirs by Flue Gas Injection

Modeling the solubility of light hydrocarbon gases and their mixture in brine with machine learning and equations of state

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

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