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

Abstract

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.

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

“…This AI approach is a multilayer feed‐forward network that designs the architecture based on a unique learning algorithm 52 . Accordingly, a combination of lease square and gradient descent is considered to decrease the possibility of getting stuck and enhance the likelihood of the optima minima 69 . To this end, membership functions (MFs) which have already been modified by the ANNs, also the fuzzy IF‐THEN rules are employed to develop a fuzzy inference system (FIS) 68 .…”

“…52 Accordingly, a combination of lease square and gradient descent is considered to decrease the possibility of getting stuck and enhance the likelihood of the optima minima. 69 To this end, membership functions (MFs) which have already been modified by the ANNs, also the fuzzy IF-THEN rules are employed to develop a fuzzy inference system (FIS). 68 Takagi-Sugeno-Kang (TSK) and Mamdani are among the well-known FIS.…”

Advancements, Challenges, and Perspectives of Flue Gas Injection in Subsurface Formations: A Comprehensive Review

“…These approaches can enhance the accuracy and efficiency of simulations, leading to improved understanding and optimization of flue gas injection processes. 120,121 Khurshid and Afgan 67 developed a simulator to estimate the formation damage especially asphaltene deposition in the flue gas injection projects with geochemical reactions in carbonate rock (Middle Eastern region). In this simulator, the governing equations of fluid flow are coupled with modified relative permeability functions for better investigation of the oil recovery factor.…”

“…By combining coding with AI and ML techniques, new approaches for modeling flue gas injection can be developed. These approaches can enhance the accuracy and efficiency of simulations, leading to improved understanding and optimization of flue gas injection processes. , …”

“…On the basis of the sensitivity analysis, pressure has more impact on the IFT between flue gas and crude oil. Additionally, temperature and the composition of the flue gas are identified as other effective parameters that affect the IFT …”