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Carbonated brine (CB) injection, known as one of the effective enhanced oil recovery processes, is highly dependent on the reservoir conditions (i.e. temperature and pressure) as well as the type of dissolved salt in aqueous solution. This study is aimed to investigate the influential parameters on the swelling of crude oil as the most important mechanism during CB injection. The swelling of crude oil in the presence of different CB solutions consisted of different salts such as KCl, NaCl, CaCl2 and MgCl2 with constant concentration of 15,000 ppm is studied using a high-pressure, high-temperature visual cell which measures the volume of drop using image processing software based on Young-Laplace method. Also, the Δ Bond number of crude oil is examined to more reliably find out the mechanisms. According to the results, it can be concluded that the solubility of CO2 in aqueous phase is not the only parameter affecting the swelling behavior, and that the crude oil and ion type as well as temperature have remarkable effect on the mobility of CO2 molecules and their partitioning from aqueous phase toward oil phase.

Unfortunately, malaria still remains a major problem in tropical areas, and it takes thousands of lives each year and causes millions of infected cases. Besides, on December 2019, a new virus known as coronavirus appeared, that its rapid prevalence caused the World Health Organization (WHO) to consider it a pandemic. As a potential drug for controlling or treating these two undesired diseases at the cellular level, chloroquine and its derivatives are being investigated, although they possess side effects, which must be reduced for effective and safe treatments. With respect to the importance of this medicine, the current research aimed to calculate the solubility of chloroquine in supercritical carbon dioxide, and evaluated effect of pressure and temperature on the solubility. The pressure varied between 120 and 400 bar, and temperatures between 308 and 338 K were set for the measurements. The experimental results revealed that the solubility of chloroquine lies between 1.64 × 10−5 to 8.92 × 10−4 (mole fraction) with different functionality to temperature and pressure. Although the solubility was indicated to be strong function of pressure and temperature, the effect of temperature was more profound and complicated. A crossover pressure point was found in the solubility measurements, which indicated similar behaviour to an inflection point. For the pressures higher than the crossover point, the temperature indicated direct effect on the solubility of chloroquine. On the other hand, for pressures less than the crossover point, temperature enhancement led to a reduction in the solubility of chloroquine. Moreover, the obtained solubility results were correlated via semi-empirical density-based thermodynamic correlations. Five correlations were studied including: Kumar & Johnston, Mendez-Santiago-Teja, Chrastil, Bartle et al., and Garlapati & Madras. The best performance was obtained for Mendez-Santiago-Teja's correlation in terms of average absolute relative deviation percent (12.0%), while the other examined models showed almost the same performance for prediction of chloroquine solubility., Highlights • Measuring solubility of chloroquine in supercritical CO2 • Thermodynamic modeling of chloroquine solubility at different conditions • Solubility of chloroquine lies between 1.64 × 10−5 to 8.92 × 10−4 (mole fraction).

Design and development of efficient processes for continuous manufacturing of solid dosage oral formulations is of crucial importance for pharmaceutical industry in order to implement the Quality-by-Design paradigm. Supercritical solvent-based manufacturing can be utilized in pharmaceutical processing owing to its inherent operational advantages. However, in order to evaluate the possibility of supercritical processing for a particular medicine, solubility measurement needs to be carried out prior to process design. The current work reports a systematic solubility analysis on decitabine as an anti-cancer medicine. The solvent is supercritical carbon dioxide at different conditions (temperatures and pressures), while gravimetric technique is used to obtain the solubility data for decitabine. The results indicated that the solubility of decitabine varies between 2.84 × 10–05 and 1.07 × 10–03 mol fraction depending on the temperature and pressure. In the experiments, temperature and pressure varied between 308–338 K and 12–40 MPa, respectively. The solubility of decitabine was plotted against temperature and pressure, and it turned out that the solubility had direct relation with the pressure due to the effect of pressure on solvating power of solvent. The effect of temperature on solubility was shown to be dependent on the cross-over pressure. Below the cross-over pressure, there is a reverse relation between temperature and solubility, while a direct relation was observed above the cross-over pressure (16 MPa). Theoretical study was carried out to correlate the solubility data using several thermodynamic-based models. The fitting and model calibration indicated that the examined models were of linear nature and capable to predict the measured decitabine solubilities with the highest average absolute relative deviation percent (AARD %) of 8.9%.

One of the effective medications for Glioblastoma Multiforme is Temozolomide (TMZ) which is taken as a treatment for some brain cancers. With respect to the importance of this drug in cancer therapy, the solubility of this medicine was analyzed under various ranges of pressure and temperature in supercritical carbon dioxide (SC-CO2) to evaluate the possibility of drug micronization for reducing its undesired side effects. The measurements were conducted using gravimetric method and revealed that the solubility of the drug is between 4.30 × 10−4 to 5.28 × 10−3 based on mole fraction. The results demonstrated the strong dependency of the TMZ solubility to pressure and temperature, while the effect of pressure is more profound at higher temperatures. Besides, the calculated solubility data was correlated employing five popular semi-empirical density-based correlations including Bartle et al., Chrastil, Kumar and Johnston, Mendez-Santiago-Teja, and Garlapati & Madras models. The obtained outcomes illustrated that among the tested models, KJ led to the best correlative ability with average absolute relative deviation percent (AARD %) of 8.39%, while Garlapati and Madras led to the worst modeling results with AARD % of 15.20%.

Particle size and morphology are among the most important parameters which must be precisely controlled in pharmaceutical industries to enhance the effectiveness of the solid dosage oral formulations. One of the most interesting methods for controlling these two quality attributes is supercritical fluid based technologies by which the drug solubility in supercritical (SC) solvent, commonly carbon dioxide, dictates whether the used method is applicable for suitable particle production or not. In this work, solubility of an anti-cancer drug namely tamoxifen in supercritical CO2 was measured under different conditions. The measurements were carried out between 120 and 400 bar pressure, and 308–338 K temperature. The measurements revealed the solubility of about 1.88 × 10−5 to 9.89 × 10−4 (mole fraction basis) for tamoxifen with strong functionality to pressure and complicated functionality to temperature. In details, pressure showed to have a direct effect on solubility, while the effect of temperature depends on existence of cross-over pressure that is about 200 bar for the case of tamoxifen. It was revealed that for the pressures greater than the cross-over point, increasing temperature led to enhancement of the tamoxifen solubility due to sublimation pressure dominanacy, while for pressures lower than cross-over point, density reduction (solvating power reduction) is the dominant mechanism as the temperature enhances. Besides the experimental measurements, the possibility of correlating and extrapolating the measured solubility of tamoxifen as a function of temperature and pressure was investigated using five well-known semi-empirical density-based models including Kumar-Johnston (K-J), Garlapati-Madras, Chrastil, Mendez-Santiago and Teja (MST), and Bartle et al., among which KJ model showed the lowest average absolute relative deviation percent of about 8.26%.

In the current study, the solubility of busulfan which is a chemotherapy drug for treating chronic myelogenous leukemia (CML) is measured in supercritical carbon dioxide (SC-CO2) at pressures between 120 and 400 bar, and temperatures between 308 and 338 K. It was indicated that the solubility of this drug via a static solubility measurement technique coupled with a gravimetric approach was between 3.27 × 10−5 to 8.65 × 10−4 (mole fraction). The measured solubility data reveled the direct effect of pressure on the solubility data, while the temperature has a dual effect of both increasing and decreasing effect considering the shifting point known as cross-over pressure which was measured to be around 160 bar for busulfan. Moreover, a theoretical approach was implemented to predict the solubility data using thermodynamic models. Indeed, five widely used semi-empirical density based correlations namely Kumar-Johnston (KJ), Bartle et al., Mendez-Santiago and Teja (MST), Garlapati and Madras and Chrastil models were used to model solubility data using only pressure, temperature and density of mixture as input parameters. The modeling as well as curve fitting results demonstrated that among the examined models, KJ model introduced more accurate correlative capability with AARD of about 7.57%.

The tunable nature of the solubility of various compounds, including molecules of pharmaceutical and biological interest, in supercritical fluids (SCFs) makes SCF extraction technology attractive for many separation and purification processes. Among the different influencing parameters, the most important one in the supercritical based processes is the knowledge of solubility of model solute. But, experimental measurement of the solubility of all pharmaceuticals in wide ranges of temperature and pressure is not only cost effective but also impossible in some cases. Regarding this fact, during the past decades, several approaches are proposed to model the solubility of the compounds in the supercritical fluids especially carbon dioxide. In this way, in the current investigation, two different approaches including five semi-empirical density based correlations (Mendez-Santiago and Teja (MST), Bartle et al., Chrastil, Kumar and Johnston (KJ) and Hezave et al.) and Peng-Robinson equation of state are used to find if it is possible to correlate the solubility of cetirizine with acceptable deviation as a function of temperature and pressure. The results reveal that among the examined approaches Hezave and Lashkarbolooki model leads to better overall correlative capability with average absolute relative deviation of 5.04% although Peng-Robinson EoS leads to lower AARD % of 3.85 % in 338 K isotherm.

One of the main concerns of each petroleum engineer is the selection of the best EOR method to maximize the oil production from the reservoir. In this regard, one of the explored-not-productive southern Iranian oil fields was considered as the objective of this study to find which enhanced oil recovery (EOR) method is the proper method to apply on this reservoir. Therefore, a procedure capable of combining the data extracted from different sources ranging from worldwide field experiences to the existing tables into a unified expert system is used. This expert system is based on Bayesian network analysis in order to sort the proper EOR techniques for further assessment by economical and environmental criteria. In addition, after collecting of surface samples at the gas and liquid separator, and subsequently recombined with solution gas oil ratio, several tests including constant composition expansion (CCE) (flash vaporization, flash liberation flash expansion, pressure-volume relations), differential vaporization (differential liberation differential expansion) and solubility and swelling tests were performed.

During the past decades, producing micro- and nano-particles of drugs is gaining attention since it is possible to modify the solubility of insoluble drugs in the gastronical fluids significantly. Respect to this fact, in the current investigation, rapid e...

In recent years, using supercritical carbon dioxide for different chemical processes in particular particle micronization has been surprisingly increased. Knowing the substance solubility in supercritical solvent is a critical parameter during the particle size reduction processes. In this direction, solubility of cephalexin was measured in the supercritical carbon dioxide by changing the temperature and pressure between 308.15 K to 338.15 K and 16 Mpa to 40 MPa, respectively. The measured solubility of cephalexin in supercritical carbon dioxide was in the range of 1.13×10-5 to 4.89 ×10-3 based on the mole fraction at the different operational condition. Besides the experimentally measurement of cephalexin solubility, modeling of the results using four commonly used correlations namely Mendez-Santiago and Teja (MST), Bartle, Kumar and Johnston and Chrastil models were performed. The results revealed that the Chrastil model is the most accurate used semi-empirical correlation in the present study with the lowest average absolute relative deviation percent of (AARD %) of 9.43 %.

Interfacial tension between crude oil and water plays a significant role in enhanced oil recovery. Many researchers seek surfactants to reduce the interfacial tension of crude oil and water that can be applicable in reservoir conditions of high salinity and temperature. The current study introduces the results of using two different families of ionic liquids (ILs) including imidazolium (1-dodecyl-3-methylimidazolium chloride ([C 12 mim][Cl]) and 1-octyl-3-methylimidazolium chloride ([C 8 mim][Cl])) and pyridinium (1-dodecyl pyridinium chloride ([C 12 Py][Cl]) and 1-octyl pyridinium chloride ([C 8 Py][Cl])) for this purpose. Measured IFT values between solutions of ILs and a southern Iranian crude oil reveal that these ILs are successful in reducing the IFT. Unlike traditional surfactants, these ILs are more effective as the salinity increases. Also the measurements revealed that there is a correlation of the IFT of the solutions with salinity and temperature.

Remaining oil after primary and secondary oil recovery processes is the target of enhanced oil recovery (EOR) methods. One of an efficient EOR method is chemical flooding changes several parameters lead to higher oil recovery. One of the parameters can be manipulated by chemical flooding (injection) is reduction of formation brine/oil interfacial tension (IFT). During the past decades, several kinds of chemicals called surfactants have been used to change the IFT of water/crude oil system. The most limitation of these surfactants is their inability to tolerate harsh conditions of salinity (ions). In the present study, functionality of a new kind of ionic liquid-based surfactants, can tolerate harsh condition of salinity, was investigated by measuring dynamic interfacial tension of water/heavy crude oil system. In this direction, 1-dodecyl-3-methylimidazolium chloride ([C12mim] [Cl]) was selected as an ionic liquid (IL)-based surfactant for the experiments and IFT measurements. The effects of salinity, NaCl concentration, temperature and concentration of IL on dynamic interfacial tension of IL solution (water + IL)/heavy crude oil system were examined. The experiments surprisingly revealed that presence of salinity leads to significant reduction of IFT of IL solution/heavy crude oil system. In addition, obtained results showed that IFT of the IL solution/heavy crude oil system was decreased as temperature was increased.

In the present study, a new correlation with both correlative and extrapolative capabilities is proposed for thermal conductivity of the pure ionic liquids (ILs) and the optimum values of its fitting parameters were obtained by a genetic algorithm. All of all, 209 data points of 21 ILs were collected from previously published literature. The collected data were divided into two different subsets namely training (143 data points) and testing (66 data points) subsets. Training subset was used to find the optimum values of the fitting parameters of the proposed correlation. After that, the extrapolative and correlative capabilities of the proposed correlation was tested by testing data subset was not utilized during the training stage. The average absolute relative deviation percent (AARD %) during the training stage was 5.22%, while the results revealed AARD % of 10.76% for testing stage. In general, the overall results revealed a rather good accuracy of the proposed correlation especially considering its extrapolative capability. Finally, the obtained results by the proposed correlation were compared with those obtained by different available correlations including group contribution and artificial neural network approach which shows a good functionality of the proposed correlation.

The use of ionic liquids is being increasingly investigated in separation technologies, for example, in special distillation processes such as azeotropic or close-boiling mixtures. Such applications require accurate knowledge of the physical properties of the mixtures involved. In this respect, the correlation or estimation of the bubble points might be difficult due to the complex nature of some ternary systems, especially in the presence of ionic liquids. In the present study, the bubble points of several ternary mixtures containing an ionic liquid were correlated using an artificial neural network modeling approach. The solvents investigated consisted of 1-propanol, 2-propanol, ethyl ethanoate, methyl ethanoate, chloroform, propanone, ethanol, methanol and water and the ionic liquids considered were 1-ethyl-3-methylimidazolium trifluoromethane-sulfonate ([emim][CF3SO3]) and 1-butyl-3-methylimidazolium tetrafluoroborate ([bmim][BF4]). For this purpose, a total of 529 experimental data points from previously published literature were collected to aid in finding the best network architecture and the optimum parameters. To do this, the collected data were divided into two subsets, namely the training and testing subsets. Using the training data set, and based on a trial and error procedure, the optimum network parameters were determined to be three layers including one input, one hidden and one output layer, nine neurons in the hidden layer, the logarithmic-sigmoid transfer function for the hidden layer, and the purline function for the output layer. The optimized weights and biases were also obtained and presented. The feasibility of the proposed network for the correlation of ternary bubble point was then examined using data that were not used in optimizing the network. The overall average absolute relative deviation (AARD %), mean square error (MSE), maximum deviation (Emax), minimum deviation (Emin) and correlation coefficient (R2) were calculated to be 0.20%, 0.9953, −0.97, 0.87 and 0.95, respectively.

Microbial enhanced oil recovery (MEOR) process utilizes microorganisms or their metabolites to mobilize the trapped oil in the oil formation after primary and secondary oil recovery stages. MEOR technique is considered as more environmentally friendly and low cost process. There are several identified mechanisms for more oil recovery using MEOR processes however; wettability alteration and interfacial tension (IFT) reduction are the important ones. Enterobacter Cloacae, a facultative bio-surfactant producer bacterium, was selected as a bacterial formulation due to its known performance on IFT reduction and wettability alteration. To quantify the effects of these two mechanisms, different tests including oil spreading, in situ and ex situ core flooding, wettability measurement (Amott), IFT, viscosity and pH measurements were performed. The obtained results revealed that the experimental procedure used in this study was able to quantitatively identify the individual effects of both mechanisms on the ultimate microbial oil recovery. The results demonstrated considerable effects of both mechanisms on the tertiary oil recovery; however after a proper shut in time period, more tertiary oil was recovered because of wettability alteration mechanism. Finally, SEM images taken from the treated cores showed biofilm formation on the rock pore surfaces, which is responsible for rock surface wettability alteration.

It is estimated that two-thirds of crude oil remains in the oil reservoirs after primary and secondary (water flooding) recovery stages. The extent of residual oil trapped in the reservoir pore structure is dominated by capillary and interfacial forces. The residual oil will be mobilized if the capillary forces are reduced because of interfacial tension (IFT) reduction during surface active agents flooding into the oil reservoir. In this work, 1-dodecyl-3-methylimidazolium chloride ([C 12 mim] [Cl]) is proposed as the IFT reducing agent and its dynamic IFT with one of the Iranian crude oil under different conditions were examined. The obtained results revealed that the critical micelle concentration (CMC) between the crude oil and formation brine was obtained at a very low concentration of 100 ppm. Unlike to the traditional surfactants, this ionic liquid based surfactant found to be more effective for higher saline formation water. In addition, several core flooding experiments were performed to find both, tertiary oil recovery efficiency and IL adsorption on the rock surfaces which indicated promising results.

Ionic liquids (ILs) have been considered as a good candidate to be replaced by the conventional solvent in recent years due to their potential consumptions and unique properties. In the present study, artificial neural network was used to predict the ternary viscosity of mixtures containing ILs. A collection of 729 experimental data points were gathered from the previously public shed literatures. Different topologies of a multilayer feed forward artificial neural network (MFFANN) were examined and optimum architecture was determined. Ternary viscosity data from the literature for 5 ILs with 547 data points have been used to train the network. In addition, to differentiate dissimilar substances, the molecular mass and boiling point temperature of the three components and two compositions of the non-ILs components were considered as input variables. It must be mentioned that due to the high boiling temperature of the ILs, most of them decomposes before achieving their boiling point. Therefore, Valderrama group contribution method was utilized to obtain the boiling points of the ionic liquids used for this study. Finally, the capability of the designed network was tested by predicting ternary viscosity of mixtures not considered during the training process of the network (182 ternary viscosity data points for 5 ILs). The results demonstrated that the proposed network was able to well predict the ternary viscosity data points even by using the predicted values of boiling temperatures of ionic liquids.

Ionic liquids (ILs) are amazing solvents gain an increasingly attention in the different areas of chemistry and chemical engineering industries during the past decade. Similar to every promising solvent, knowing the physiochemical properties of the ILs seems to be crucial to develop new designed ILs based industries. In this direction, the present study extends an artificial neural network (ANN) to correlate the binary heat capacity of ILs. To verify the proposed network, 1571 binary heat capacity data points were collected from the previously published literatures and divided in to two subsets namely training and testing subsets. The optimum parameters of the network including the number of hidden layer, numbers of neurons and transfer functions in hidden and output layers were obtained using these subsets data points. In addition, the predictive capability of the optimized network was validated using the testing data points (not considered in the training stage). The optimized network configuration consisted of one hidden layer with sixteen neurons and tansig and purelin transfer functions for the hidden and output layers. The obtained results from the training and test stages show that the proposed network was able to accurately predict the binary heat capacity of ILs binary mixtures with total absolute average relative deviation (AARD%) of 1.60% and relation coefficient ( R 2 ) value of 0.9975.

The unique physical properties of ionic liquids play a decisive part in many of their applications. Therefore, the ability to predict the physical properties of ionic liquids is extremely important for the rational design of proper ionic liquids with specific properties. In practice, the processes involving ionic liquids usually contain other components, in addition to the ionic liquids. Therefore, in addition to pure component properties, knowledge of the physical properties of mixtures are also crucial for various applications. In the present study, the feasibility of using a feed-forward multi-layer perceptron neural network (MLPNN) model was investigated to predict the electrical conductivity of the ternary mixtures of 1-butyl-3-methylimidazolium hexafluorophosphate ([bmim][PF 6 ]) + water + ethanol and [bmim][PF 6 ] + water + acetone in the temperature range from 288.15 K to 308.15 K, consisting of 104 data points. Not only were different networks, namely the linear and the hyperbolic tangent sigmoid transfer functions, considered in this study, but also the effects of the number of hidden layers, hidden neurons and the training algorithm were investigated on the accuracy of the results using 78 data points as training data to minimize the average absolute relative deviation percent (AARD%), mean square error (MSE) and correlation coefficient ( R 2 ). Among the various cases studies, statistical analyses indicated the best configuration of the network to include one hidden layer and seven neurons in the hidden layer. The optimum network was then validated using 26 data points (test data) not used in the training stage which indicated the good interpolative ability of the trained network with AARD% = 1.44, MSE = 2.87 × 10 −8 and R 2 = 0.9981.

The main goal of this study was to measure the solubility of mefenamic acid in supercritical carbon dioxide (SC-CO2) at pressures and temperatures range of 16–40 MPa and 308.15, 318.15, 328.15 and 338.15 K, respectively. The obtained results revealed that the mole fractions were varied in the range of 8.31 × 10−5–5.98 × 10−3 under the different conditions. The obtained results were correlated with four empirical correlations including Chrastil, Mendez-Santiago–Teja (MST), Bartle and Kumar and Johnston (K–J) models. The optimized parameters of these correlations were obtained by the graphical regression. The calculated results show a good consistency between the measured and calculated solubilities for Chrastil, MST, Bartle and Kumar and Johnston models with average absolute relative deviation percent (AARD%) of 5.84%, 3.93%, 4.79%, and 5.20%, respectively. In addition, experimental results were modeled with two equations of state including the Esmaeilzadeh–Roshanfekr (ER) and Peng–Robinson (PR) EoSs which their binary interaction parameters, kij and lij, were optimized by the differential evolution (DE) method. DE is a very simple population based stochastic function minimizer which is very powerful at the same time. The estimated solubilities show maximum AARD% of 21.25% and 30.56% and minimum AARD% of 3.54% and 6.81% were found for the ER and PR-EOS's, respectively.

The particle size of organic and inorganic materials is vital parameter to determine its final use. Most of the newly developed pharmaceutical materials are poorly soluble or insoluble in the aqueous media such as biological fluids. Particle size reduction of such pharmaceuticals is one of the clues to improve the dissolution rate, adsorption and bioavailability. In this study, the effect of extraction and expansion parameters of the RESS process such as extraction temperature (313–333 K), extraction pressure (140–230 bar), effective nozzle diameter (450–1700 μm), nozzle length (2–15 mm) and spraying distance (1–7 cm) on the size and morphology of the precipitated particles of cephalexin were investigated. The morphology and particle size of the unprocessed and processed (precipitated) particles were examined by the SEM images. The mean particle size of the precipitated particles was between 0.86 and 7.22 μm depending upon the different experimental conditions used. The precipitated cephalexin particles were close to spherical form while the unprocessed particles were irregular or needle in shape.

The particle size of the pharmaceutical substances is important for their bioavailability (the percentage of the drug absorbed compared to its initial dosage). The absorption rate can be increased by reducing particle size of the drug particles. This study was conducted to investigate the effects of the extraction pressure (140–220 bar), extraction temperature (308–338 K), nozzle length (2–15 mm), effective nozzle diameter (450–1700 μm), and collection distance (1–10 cm) on the size and morphology of the precipitated ketoprofen particles. The characterization (size and morphology) of the particles was investigated using scanning electron microscopy (SEM). The average particle size of the original material was 115.42 μm, while the average particle size of the micronized particles is between 0.35 and 7.03 μm near to quisi-spherical, needle and irregular shape depending upon the experimental conditions.

During any microbial enhanced oil recovery process, both cells and the metabolic products of bacteria govern the tertiary oil recovery efficiency. However, very accurate examination is needed to find the functionality of these tiny creatures at different reservoir conditions. In this regard, the effect of cell structure on ultimate microbial recovery efficiency which is the most dominant mechanism based on the microorganism types (gram-negative or gram-positive) was systematically investigated. At the first stage, possible different active mechanisms using Bacillus stearothermophilus SUCPM#14 strain were tested using specially designed injection protocol, in situ and ex situ core flooding experiments, interfacial tension, viscosity, pH and Amott wettability index measurements. At the second stage, comparing functionality of B. stearothermophilus SUCPM#14 (a gram-positive type) with the previously examined strain namely Enterobacter cloacae as a gram-negative type, proposed this hypothesis that the cell structure significantly affects the interfacial behaviors. New designed protocols were utilized to check the individual effects of cells, bioproducts and interaction of these together on the oil/water and also fluids/rock interfaces. The final results showed that the cells of B. stearothermophilus SUCPM#14 adhere more into the oil/water interface compared to E. cloacae and change its rheological properties; e.g. its elastic properties which affect the ultimate microbial oil recovery efficiency. Eventually, contradicting results revealed that biosurfactant produced by E. cloacae was able to considerably reduce the interfacial tension and alter the wettability of the rock (to neutral conditions) while biosurfactant produced by B. stearothermophilus SUCPM#14 was not very effective.