Prediction Of Interfacial Tension Research Articles

White-Box Machine Learning Models for Accurate Interfacial Tension Prediction in Hydrogen-Brine Mixtures

Abstract The severity of climate change and global warming necessitates the need for a transition from traditional hydrocarbon-based energy sources to renewable energy sources. One intrinsic challenge of renewable energy sources is their intermittent nature, which can be addressed by transforming excess energy into hydrogen and storing it safely for future use. To securely store hydrogen underground, a comprehensive knowledge of the interactions between hydrogen and residing fluids is required. Interfacial tension is an important variable influenced by cushion gases such as CO2 and CH4. This research developed explicit correlations for approximating the interfacial tension of hydrogen-brine mixture using two advanced machine learning techniques: gene expression programming and the group method of data handling. The Interfacial tension of hydrogen-brine mixture was considered as heavily influenced by temperature, pressure, water salinity, and average critical temperature of the gas mixture. The results indicated a higher performance of the group method of data handling-based correlation, showing an average absolute relative error of 4.53%. Subsequently, Pearson, Spearman, and Kendall methods assessed the influence of individual input variables on the correlations’ outputs. Analysis showed that temperature and average critical temperature of the gas mixture had considerable inverse impacts on the estimated interfacial tension values. Finally, the reliability of the gathered databank and the scope of application for the proposed correlations were verified by the leverage approach by illustrating 97.6% of the gathered data within the valid range of the Williams plot.

Application of deep learning through group method of data handling for interfacial tension prediction in brine/CO2 systems: MgCl2 and CaCl2 aqueous solutions

Capillary/residual CO2 trapping is one of the main mechanisms of CO2 storage in underground formations. Therefore, it is required to estimate the brine/CO2 interfacial tension under different conditions. Although many methods have been proposed so far, the error of estimation is still high. This paper proposes a novel deep learning method to estimate the brine/CO2 interfacial tension at various temperatures, pressures, and salinities. The proposed method is a neural network with the Group Method of Data Handling (GMDH) learning method. The GMDH has the advantage of handling the structural and parametric optimization of the network automatically. The proposed method is tested on an experimental dataset of brine/CO2 interfacial tension with CalCl2 and MgCl2 salts. The results of the proposed method were compared with four of the best performing methods in the literature. The Average Absolute Percentage Error (AAPE) of the method on the training, testing and all data was 1.3 %, 2.95 %, 1.73 %, respectively, while the best method from the literature could reach an AAPE of 8.16 % on all data. Therefore, the proposed method performs far better than the existing methods. Also, a sensitivity analysis was done to determine the most influential inputs to estimate the output. The contribution of this work is to show the applicability of the GMDH method to construct more optimal data-driven models to estimate the brine/CO2 interfacial tension. Also, the utilized dataset is collected under a wide range of pressure, temperature and salinity conditions that increases the generality of the model.

Application of Heterogeneous Ensemble Learning for CO2–Brine Interfacial Tension Prediction: Implications for CO2 Storage

Application of Heterogeneous Ensemble Learning for CO₂–Brine Interfacial Tension Prediction: Implications for CO₂ Storage

Lessons Learned in Interfacial Tension Prediction Using a Mixture of Sulfonate- and Ethoxylate-based Surfactants in a Waxy Oil-brine System

The chemical-enhanced oil recovery (CEOR) method is applied to change reservoir rock or fluid characteristics by injecting alkaline, surfactant, and polymer or a combination of two or three of the compounds. Surfactant flooding improves oil recovery by reducing the interfacial tension between oil and water. Selecting reservoir surfactants, especially microemulsions, requires careful screening. This study predicted waxy oil system interfacial tension using surfactant mixtures at below- and above-optimum salinity. To predict the interfacial tension, microemulsion types, HLB, ideal salinity, and HLD were used. The study predicted oil-surfactant-water interfacial tension using SAE, FEO, and their mixtures. We improved the Huh equation by adding a fitting parameter, β, to accommodate the transition from type III to type II microemulsions as salinity increases. With increasing salinity, anionic surfactant’s hydrophilic-hydrophobic interactions change, affecting the values and surfactant layer thickness. This study improved hydrophilic-lipophilic deviation (HLDN) by establishing a fixed interval for nonionic surfactants. Van der Waals attraction, values and interface surfactant layer thickness are connected, reflecting the fact that lower values reduce interfacial tension better. This study also found that surfactant packing at the oil-water interface increases the order of the oil-solution ratio and the microemulsion values with polarity.

Estimation of CO2-Brine interfacial tension using Machine Learning: Implications for CO2 geo-storage

Carbon capture and storage (CCS) is a promising technique to reduce anthropogenic gases causing climate change. This efficient strategy contributes toward reaching net-zero emissions and consists of capturing CO2, transporting it, and sequestering it deep down into selected geological formations. Subsurface storage of carbon dioxide (CO2) depends on several factors like injectivity, formation characteristics, seal integrity, and the associated rock-fluid and fluid–fluid interactions, etc. One critical parameter, in this context, is the interfacial tension (IFT) of the fluid–fluid system in question i.e., CO2-brine IFT for CO2 geo-storage. While experimental data for IFT of CO2-brine systems have been rigorously reported, and a few studies generated robust correlations to forecast the IFT as a function of its influencing factors, still the correlations lack in terms of accuracy and consideration of the most up-to-date data inventory. This paper thus presents a robust and accurate artificial intelligence (AI) based model to estimate the IFT of CO2-brine systems based on the largest data set (2896 points) utilized so far. A range of intelligent models such as Gradient Boosting (GB), Neural Network (NN), and Genetic Programming (GP) are used here to predict CO2-brine IFT. Furthermore, the most influencing factors are evaluated by using the relevance factor analysis method that helps in determining the weight of the contribution of each parameter on IFT. Our results suggest that: a) Gradient Boosting (GB) model with all its derivatives demonstrates the best accuracy for IFT prediction with a high coefficient of determination (R2) equal to 0.964, b) lowest performance is attributed to GP, and c) the impact of different factors is found to be in the order pressure > temperature > salinity > impurities. Moreover, an improved IFT correlation as a function of thermophysical and chemical properties i.e., temperature, pressure, and salinity is presented to quantify IFT with high precision (R2 = 0.886 and MRAE = 0.295) and significant time saving. This correlation is further validated and results show that it can capture the several chemical and physical processes leading to the various behavior trends of IFT stated in the literature. As a direct application in CO2 geo-storage projects, our proposed correlation is used to determine the optimal storage depth of a real carbonate saline aquifer located onshore of UAE. This study thus provides a robust model to estimate CO2-brine IFT which is important for storage capacity estimations and helps to better understand the factors influencing IFT. The model proposed here captures the dependence of CO2-brine IFT on six independent variables including pressure, temperature, brine ionic strength, cation type, and presence of impurities (CH4 and N2).

A prediction of interfacial tension by using molecular dynamics simulation: A study on effects of cushion gas (CO2, N2 and CH4) for Underground Hydrogen Storage

Carbon Dioxide (CO2) emissions from fossil fuel consumption have caused global warming and remain challenging problems for mitigation. Underground Hydrogen Storage (UHS) provides clean fuel and replaces traditional fossil fuels to reduce emissions of CO2. Geological formations such as depleted oil/gas reservoirs, deep saline aquifers and shale formations have been recognized as potential targets to inject and store H2 into the subsurface formations for large-scale implementation of CCS and UHS. However, the presence of H2 with cushion gas at different fractions under different geo-storage conditions, which can influence Hydrogen's flow properties, was not investigated widely. Until now, studies of interfacial properties between water and a mixture of cushion gas (CO2, N2 or CH4) in the presence of H2 are very limited or unavailable data in experiments and simulations. In this study, many predictions by using molecular dynamics simulation were conducted to predict the interfacial tension (γ) for the systems of H2/CO2/H2O, H2/N2/H2O and H2/CH4/H2O at different pressures, temperatures, and fractions of cushion gases A comparison between the predicted γ results from the simulation and previous research were also made. The findings of this study indicated that γ of H2/CO2/H2O, H2/CH4/H2O, and H2/N2/H2O, as a function of pressure, temperature, and fraction of H2, decreased with increasing pressures and temperatures and increased with increasing H2% in the mixture. Additionally, an extending or new γ data in simulation for the CO2/H2/H2O, N2/H2/H2O and CH4/H2/H2O systems from this study were reported and support evaluating the stability and storage capacity of H2 combined with the cushion gas in geological formations. Furthermore, it can contribute to de-risking and proceeding safely and efficiently for the large-scale implementation of Underground Hydrogen Storage.

New-generation machine learning models as prediction tools for modeling interfacial tension of hydrogen-brine system

Recently, hydrogen (H2) gas has gained prodigious attention as a sustainable energy carrier to reduce acute dependence on fossil fuels due to its fascinating properties. To ensure it continuous availability, hydrogen storage in underground geologic formations has been proffered. Nonetheless, H2 storage in underground formations is dependent on fluid-fluid interfacial tension (IFT). Herein, new-generation machine learning models namely Gaussian Process Regression (GPR), the Elman Neural Network (ENN), and the Logistic Regression (LR) were used to predict the IFT of the H2-brine system. For this purpose, the includes temperature (T), pressure (p), and density difference (Δρ), with the surface tension (γ) as the output variable. The effectiveness of each model was assessed through a variety of metrics including the Nash-Sutcliffe efficiency (NSE), the Mean Absolute Error (MAE), the Mean Absolute Percentage Error (MAPE), the Correlation Coefficient (PCC), the Root Mean Square Error (RMSE), and BIAS. Moreover, the limitations of traditional chemometrics feature extraction was overcome by utilizing an original linear matrix input-output (M1-M3) feature extraction approach. The result generated demonstrates that the suggested models and correlation offer sterling IFT estimations. The Gaussian Process Regression (GPR) model outperformed the other evaluated machine learning methods. Particularly, the GPR-M2 model combination showed extraordinary effectiveness, outperforming the BTA-M1 model, which had the lowest performance by 22%. Numerical comparison indicated that GPR-M2 with MAPE = 0.0512, and MAE = 0.002 emerged as the best reliable model. This study extends the frontier of knowledge in achieving carbon-free and sustainable energy society via accurate IFT prediction of H2-brine system.

A-Priori screening of deep eutectic solvent for enhanced oil recovery application using COSMO-RS framework

This study investigates the potential of Deep Eutectic Solvent (DES) in Enhanced Oil Recovery (EOR) via interfacial tension (IFT) reduction mechanism. The effectiveness of 217 DES in the IFT reduction mechanism that ultimately assist in EOR is investigated via COnductor-like Screening MOdel for Real Solvent (COSMO-RS) theory and the sigma moment model. Genetic algorithm (GA) is employed to estimate the constants of COSMO-RS theory and sigma moment model. IFT prediction of representative Omani heavy crude oil in 217 DES in the brine system is reported and analyzed at the mole fraction of DES in brine ranging from 1 × 10-3 to 5 × 10-1 with variation in temperature from 298.15 K to 353.15 K. The highest and the lowest values thus obtained for IFT are 560.8 and 0.2 mN/m. There are 26 DESs capable of reducing IFT by more than 50%, while another 10 DESs show a reduction capability of more than 75%. The optimum concentration of DES to maximize IFT reduction is further analyzed.

New Prediction Model of Surface and Interfacial Energies Based on COSMO-UCE.

The nature and strength of intermolecular and surface forces are the key factors that influence the solvation, adhesion and wetting phenomena. The universal cohesive energy prediction equation based on conductor-like screening model (COSMO-UCE) was extended from like molecules (pure liquids) to unlike molecules (dissimilar liquids). A new molecular-thermodynamic model of interfacial tension (IFT) for liquid-liquid and solid-liquid systems was developed in this work, which can predict the surface free energy of solid materials and interfacial energy directly through cohesive energy calculations based on COSMO-UCE. The applications of this model in prediction of IFT for water-organic, solid (n-hexatriacontane, polytetrafluoroethylene (PTFE) and octadecyl-amine monolayer)-liquid systems have been verified extensively with successful results; which indicates that this is a straightforward and reliable model of surface and interfacial energies through predicting intermolecular interactions based on merely molecular structure (profiles of surface segment charge density), the dimensionless wetting coefficient RA/C can characterize the wetting behavior (poor adhesive (non-wetting), wetting, spreading) of liquids on the surface of solid materials very well.

A new molecular thermodynamic model of liquid-liquid interfacial tension

• A new molecular thermodynamic model of interfacial tension is derived. • This model yields satisfactory prediction results for interfacial tension. • It doesn't require mutual solubility data for interfacial tension predictions. • This model is simple in implementation and reliable in application. Interfacial tension between water and organic liquids plays an important role in the design and optimization of various processes, such as enhanced oil recovery, the entrapment and migration of non-aqueous phase liquid (NAPL), chemical separation technology (liquid-liquid extraction) relevant to ex-situ hazardous waste remediation and chemical processing. A new molecular thermodynamic approach of monolayer interface model was developed in this work, in which the total interfacial energy includes contributions from the excess interface cohesive energy and excess interface entropy (entropy from partially mixing and entropy from free volume change). The accuracy of this model in the prediction of interfacial tension for water-organic liquid and mercury-nonmetallic liquid systems has been examined with satisfactory results. The investigation results indicates that for water-organic liquid systems, the excess interface entropy term makes considerable contribution (9.5%∼46.6%) to the total interfacial energy, which may result in the interaction parameter of Girifalco and Good model being greater than unity (1.04≤ Φ ≤1.15) for water-alcohols; while for mercury-nonmetallic liquid systems, the contribution to the total interfacial energy from interfacial entropy is negligible (1.2%∼2.7%), the difference in cohesive energy of molecules between the bulk phase and interfacial phase dominates the wetting process. This method doesn't require measured mutual solubility data for interfacial tension predictions, which is simple in implementation and reliable in application.

Correlation between Phase Behavior and Interfacial Tension for Mixtures of Amphoteric and Nonionic Surfactant with Waxy Oil

Phase behavior tests in the surfactant screening process for EOR applications remain one of the relatively convenient ways to design an optimum surfactant formulation. However, phase behavior studies are unable to provide quantitative data for interfacial tension, which is one of the parameters that must be considered when selecting surfactants for EOR. Several studies related to the prediction of interfacial tension through phase behavior testing have been carried out. In this paper, the Huh correlation was used to estimate the interfacial tension value based on phase behavior tests. It was found that the current form of the Huh correlation may be applied for the below-to-optimum salinity condition. Furthermore, the constants of the equation vary depending on the surfactant type and mixtures.

Thermodynamic prediction of interfacial tension of water/oil system with the presence surfactants and salt

Thermodynamic prediction of interfacial tension of water/oil system with the presence surfactants and salt

Molecular Dynamics Simulations of the Vapor-Liquid Equilibria in CO2/n-Pentane, Propane/n-Pentane, and Propane/n-Hexane Binary Mixtures.

Molecular dynamics (MD) simulations were used to study vapor-liquid equilibrium interfacial properties of n-alkane and n-alkane/CO2 mixtures over a wide range of pressure and temperature conditions. The simulation methodology, based on CHARMM molecular mechanics force field with long-range Lennard-Jones potentials, was first validated against experimental interfacial tension (IFT) data for two pure n-alkanes (n-pentane and n-heptane). Subsequently, liquid-vapor equilibria of CO2/n-pentane, propane/n-pentane, and propane/n-hexane mixtures were investigated at temperatures from 296 to 403 K and pressures from 0.2 to 6 MPa. The IFT, liquid and vapor phase densities, and molecular compositions of the liquid and vapor phases and of the interface were analyzed. The calculated mixture IFTs were in excellent agreement with experiments. Likewise, calculated phase densities closely matched values obtained from the equation of state (EOS) fitted to the experimental data. Examination of the density profiles, particularly in the liquid-vapor transition regions, provided a molecular-level rationalization for the observed trends in the IFT as a function of both molecular composition and temperature. Finally, two variants of the empirical parachor model commonly used for predicting the IFT, the Weinaug-Katz and Hugill-Van Welsenes equations, were tested for their accuracy in reproducing the MD simulation results. The IFT prediction accuracies of both equations were nearly identical, implying that the simpler Weinaug-Katz model is sufficient to describe the IFT of the studied systems.

On the Evaluation of Interfacial Tension (IFT) of CO2–Paraffin System for Enhanced Oil Recovery Process: Comparison of Empirical Correlations, Soft Computing Approaches, and Parachor Model

Carbon dioxide-based enhanced oil-recovery (CO2-EOR) processes have gained considerable interest among other EOR methods. In this paper, based on the molecular weight of paraffins (n-alkanes), pressure, and temperature, the magnitude of CO2–n-alkanes interfacial tension (IFT) was determined by utilizing soft computing and mathematical modeling approaches, namely: (i) radial basis function (RBF) neural network (optimized by genetic algorithm (GA), gravitational search algorithm (GSA), imperialist competitive algorithm (ICA), particle swarm optimization (PSO), and ant colony optimization (ACO)), (ii) multilayer perception (MLP) neural network (optimized by Levenberg-Marquardt (LM)), and (iii) group method of data handling (GMDH). To do so, a broad range of laboratory data consisting of 879 data points collected from the literature was employed to develop the models. The proposed RBF-ICA model, with an average absolute percent relative error (AAPRE) of 4.42%, led to the most reliable predictions. Furthermore, the Parachor approach with different scaling exponents (n) in combination with seven equations of state (EOSs) was applied for IFT predictions of the CO2–n-heptane and CO2–n-decane systems. It was found that n = 4 was the optimum value to obtain precise IFT estimations; and combinations of the Parachor model with three-parameter Peng–Robinson and Soave–Redlich–Kwong EOSs could better estimate the IFT of the CO2–n-alkane systems, compared to other used EOSs.

Molecular structure incorporated deep learning approach for the accurate interfacial tension predictions

Characterization of the interface of a two-phase system by interfacial tension (IFT) imposes a great impact on the chemical and environmental engineering. In this work, a deep neural network (DNN) approach was developed to estimate IFT of water-hydrocarbon and water-alcohol interfaces. The predictive power of this approach for IFT was found to be much more improved than those of the previously proposed empirical correlations, both qualitatively and quantitatively. The input vector of two-phase systems generally contains five parameters, including critical temperature, critical pressure, and density difference, in addition to temperature and pressure. In this approach, a line notation describing the molecular structure of chemical species was also taken as an input. The most accurate results with the root-mean-square error (RMSE) of 1.28 mN/m are acquired as all six parameters are included. However, our analyses show that density difference and molecular structure are much more important than the critical properties. As a result, the DNN approach with the input vector involving molecular structure, temperature, and pressure only is able to yield sufficiently accurate results (RMSE 1.71 mN/m), and can successfully depict the descending, ascending, and concave dependences of IFT on temperature.

Modeling of interfacial tension in binary mixtures of CH4, CO2, and N2 - alkanes using gene expression programming and equation of state

Interfacial tension (IFT) plays a key role in enhanced oil recovery (EOR) processes that involve the injection of light hydrocarbon and non-hydrocarbon gases into oil reservoirs. Crude oil is a complex mixture of different hydrocarbons, its phase behavior is unknown in presence of miscible gas under reservoir conditions; therefore, most of the IFT measurement experiments are done using a certain fluid, such as n-alkanes. CO2 is a common gas used for EOR purposes. Since N2 and CH4 impurities in the injected CO2 stream substantially influence the IFTs and the MMP of the system, several experimental studies conducted on (N2 + n-alkane) and (CH4 + n-alkane) systems over a wide range of temperature and pressure to provide a comprehensive IFT database. Accurate prediction of this property under the thermodynamic conditions encountered in petroleum reservoirs is of paramount importance for achieving the highest amount of oil recovery. In this study, two different models are developed for prediction of IFT in binary mixtures containing (CO2 + n-alkane), (N2 + n-alkane), and (CH4 + n-alkane). These models include the thermodynamic-based model of Weinaug and Katz 1943 [1] combined with PR-EoS, and the mathematical-based model of Gene Expression Programming (GEP). A preprocessing based on Z-score method is applied on the assembled dataset to remove the outliers and duplicates from the data. The GEP and EoS models were able to predict accurate IFT results in all three systems with the EOS providing slightly inferior results in systems containing N2 gas. For some experimental observations the EoS model failed to provide any IFT results. In addition, to have a better understanding of the performance of the developed models, they were compared for IFT values less and greater than or equal to 1 mN/m for all the systems. The average squared coefficient of determination (R2) for the GEP model in CH4, CO2, and N2 – alkanes systems were 0.92, 0.94, and 0.91, and for EoS model were 0.94, 0.87, and 0.66, respectively. The findings of this study can help for a better understanding of interfacial tension under the thermodynamic conditions encountered in petroleum reservoirs is of paramount importance for achieving the highest amount of oil recovery.

On the study of the vapor-liquid interface of associating fluids with classical density functional theory

Classical density functional theory (DFT) is applied to study the vapor-liquid interface of associating fluids. Our classical DFT implementation uses a Helmholtz free energy functional that reduces to the perturbed-chain statistical associating fluid theory (PC-SAFT) equation of state in the bulk limit. We apply a new association functional, presented in one of our previous works, based on the weighted density approximation to calculate the interfacial tension of associating fluids and compare them with two of the most used association functionals from literature. It is found that classical DFT with the three association functionals and the current molecular parameters of PC-SAFT is not able to predict satisfactorily the interfacial tension of alkanols. Therefore, the experimental interfacial tension of alkanols and some associating fluids is included in the determination of the pure molecular parameters of PC-SAFT. With this approach the determination of interfacial tension of pure associating fluids can be greatly improved with the new parameters, but at the expense of slightly higher deviations in the vapor pressure. The new parameters are tested for the determination of vapor-liquid equilibria (VLE) and the prediction of interfacial tension of binary mixtures containing at least one associating compound. Good agreement is found between experimental data of VLE and interfacial tension of the binary mixtures and the three association functionals. Furthermore, we discuss about the limitations and capabilities of the current classical DFT implementation in the study of the vapor-liquid interface of associating fluids.

Experimental and modeling investigations of the interfacial tension of three different diesel + nitrogen mixtures at high pressures and temperatures

Interfacial tension (IFT) data are reported at temperatures up to 530 K and pressures up to 100 MPa for mixtures of N2 with a highly paraffinic diesel, an ultra-low sulfur diesel, and a highly aromatic diesel. The impact of composition on the IFT is determined by comparing data for each system at the same temperature and pressure calculated from a Tait-like relationship fit to the experimental data. The greatest differences in diesel + N2 IFT values are found near the normal boiling point (Tboil) of each diesel where lower molecular weight diesel components begin to dissolve in the N2-rich vapor phase. IFT data are modeled with density gradient theory (DGT) coupled with the perturbed-chain, statistical associating fluid theory equation of state (EoS). Diesel EoS parameters are calculated using three variations of a pseudo-component technique created from one group contribution (GC) method developed from high-pressure density data and from two other GC methods (S-GC and T-GC) developed from differing sets of pure component vapor pressure and saturated liquid density data. The IFT predictions significantly improve when the DGT influence parameter, cii, is allowed to vary with temperature. The DGT predictions with the B-GC method are in closest agreement with data at temperatures below Tboil, which is attributed to the more accurate representation of liquid phase densities. However, at temperatures above Tboil, where significant amounts of low molecular weight diesel components dissolve in the N2-rich vapor phase, DGT predictions with the S-GC and T-GC methods provide the closest agreement with IFT data.

Two-Layer Ensemble-Based Soft Voting Classifier for Transformer Oil Interfacial Tension Prediction

This paper uses a two-layered soft voting-based ensemble model to predict the interfacial tension (IFT), as one of the transformer oil test parameters. The input feature vector is composed of acidity, water content, dissipation factor, color and breakdown voltage. To test the generalization of the model, the training data was obtained from one utility company and the testing data was obtained from another utility. The model results in an optimal accuracy of 0.87 and a F1-score of 0.89. Detailed studies were also carried out to find the conditions under which the model renders optimal results.

Interfacial Tension Prediction of Readily-Mixed Waxy Crude Oil Emulsion at Pour Point Temperature

In the industry, stubborn emulsion still constitutes up to 20% of the total emulsion volume. The existing remediation strategies for emulsion treatment rely heavily on the study of heavy crude oil emulsion. However, minimal information is available on integrating interfacial rheology with emulsion stability on waxy crude oil emulsion. The proposed research provides a study to the development of integration between interfacial rheology and emulsion stability so that it can be a quick assessment but an accurate method to measure emulsion stability. The primary objectives of the research are to provide an extensional study to the design development of a comprehensive interfacial rheology protocol for the assessment of emulsion stability by developing a method of testing and monitoring the interfacial rheology and to investigate the demulsification ability of the waxy crude oil emulsion subjected to microbial treatment. The novelty of this study is to use the newly developed measurement protocol via interfacial rheology to predict emulsion stability. Application of the microbes on waxy crude oil to breakdown the water-in-oil emulsion using a rheometer will also be explored. The treatment is targeted to disintegrate the interfacial layer within the emulsion leading to better oil recovery. Rheological properties of the emulsion will be monitored upon the microbial injection to analyze the effects of the treatment on the rheology of emulsion. The outcomes from this research is that the newly developed protocol will predict emulsion stability that could resolve the stubborn emulsion issues via the developed interfacial rheology protocol, which could be time-saving and increases the production efficiency. This research paper is a study to develop a correlation on surface tension and interfacial tension between crude oil, water and a readily-mixed emulsion.