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.
Highlights
To produce crude oil from a reservoir, three methods are available: primary, secondary, and tertiary or enhanced oil recovery (EOR)
Different gases are injected into the reservoir, which consists of flue gases, hydrocarbons, air, N2, CO2, and a mixture of gases
In order to create a fitting network in multilayer perceptron (MLP), the number of hidden layers and the number of neurons in every hidden layer are determined by applying a trial-and-error approach
Summary
To produce crude oil from a reservoir, three methods are available: primary, secondary, and tertiary or enhanced oil recovery (EOR). The reduction of interfacial tension (IFT) can help to increase EOR by removing the trapped oil. It can increase the two-phase miscibility and improve oil recovery [2,3]. The ability of CO2 to interact with the reservoir fluid makes it attractive to use for this specific purpose. Another advantage of CO2 injection is storing it underground, as CO2 is a greenhouse gas. The cyclic injection ability and accessibility of N2 in the air, as well as its low cost, makes it an attractive option for EOR operations. As the main emissions of industrial operations, contains mainly CO2 and N2 along with
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