Prediction of the interfacial tension between hydrocarbons and carbon dioxide

In the recent years due to increasing demand for energy and declination of reservoir production, an impressive notice on enhancement of oil recovery has been found. The gas injection especially carbon dioxide injection due to low cost and friendly environmentally of this approach the special attention to CO₂ injection increased. The miscibility is known as key factor which effects on enhancement of recovery. The miscibility is controlled by interfacial tension of hydrocarbons and carbon dioxide so the importance of investigation of the interfacial tension becomes highlighted.in this investigation by using radial basis function (RBF) artificial neural network (ANN) as a novel approach the interfacial tension of hydrocarbons and carbon dioxide in terms of pressure, temperature, liquid and gas densities and molecular weight of alkane. The graphical and statistical results illustrated the fact that RBF-ANN algorithm is applicable for estimation of interfacial tension between hydrocarbons and carbon dioxide with great accuracy.     

Experimental investigation of interfacial tension and oil swelling for asphaltenic crude oil/carbonated water system

The phenomenon of oil swelling at the oil-carbonated water (CW) system could be an important mechanism during the water alternating gas (WAG) injection process. Nevertheless, the study of the main mechanisms during water flooding (WF) is a complex topic that has not been well revealed so far, especially for asphaltenic crude oil (ACO) systems. Hence, the main goal of this experimental work is to determine the influence of carbon dioxide (CO₂) within the water phase in the interfacial tension (IFT) between water and crude oil for an extensive range of pressures between 400 psi and 2000 psi (i.e. 2.76–13.79 MPa), under two temperatures of 313.15 and 323.15 K (i.e. 40 and 50 °C) by axisymmetric drop shape analysis (ADSA) method. The experimental results demonstrate that the water/ CW and crude oil IFTs decline with time. The value of dynamic IFT (DIFT) between CW and crude oil decreased about 6 mN/m in comparison with the oil–water DIFT. As a result of the CO₂ solubility, the crude oil droplet swells with increasing pressure. When the temperature rises, the effects of increasing entropy phenomena and decline of liquids density is dominant compared to the solubility of CO₂. Thus, the volume of oil droplet increases with temperature, unexpectedly. In addition, as thetemperature increases the water/CW-Oil IFT is slightly reduced over a wide range of pressure evaluated. Nevertheless, there is a slight increase as the pressure increases for the water–oil system. According to the predicted results, interfacial tension of the CW-oil system declines with increasing pressure until the solubility of CO₂ is reached to a maximum value and then approximately remains changeless.     

Modeling of CO2 absorbtion by amino acid salts blended with amine solutions

In this work, the goal is modeling of carbon dioxide loading capacities by exploiting artificial neural network model in two applicable amino acid salt solutions blended with amine solutions as an additive in wide ranges of temperature and pressure. In this regard a group of 740 experimental data points for CO₂ loading capacity has been collected from recent literature work. Results of a developed network show the good capability to predict CO₂ loading capacity in solutions with Average Relative Deviation equal to 3.8608, Mean Square Error value of 0.0045 and correlation coefficient equal to 0.9976.     

Modeling of carbon dioxide capture from gas stream emissions using amino acid salts blended with amine solutions

This work is aimed at modeling the carbon dioxide (CO₂) loading capacities by exploiting an artificial neural network model in two applicable amino acid salt solutions blended with amine solutions as an additive over a wide range of temperature and pressure. In this regard, a group of 740 experimental data points for CO₂ loading capacity has been collected from recent literature works. The results show that the developed network has good capability to predict CO₂ loading capacity in solutions with average relative deviation equal to 3.8608, mean square error value of 0.0045, and correlation coefficient equal to 0.9976.     

Application of ANFIS-GA as a novel and accurate tool for estimation of interfacial tension of carbon dioxide and hydrocarbon

In the recent years, the enhancement oil recovery processes become the one of the interesting topics in petroleum engineering because of declination of oil reservoirs. One of the most popular processes is the carbon dioxide injection that has special importance because of its environmentally friendly and high efficiency of displacement. The interfacial tension (IFT) between carbon dioxide and hydrocarbon is known as a key parameter in this process so in the present investigation the Adaptive neuro-fuzzy inference system (ANFIS) was coupled with Genetic Algorithm (GA) to create a novel tool for prediction IFT between carbon dioxide and hydrocarbon in terms of temperature, pressure, molecular weight of alkane, gas and liquid densities. The outputs of predicting model were compared with experimental IFT statistically and graphically. The comparisons showed that predicting model has acceptable accuracy in prediction of IFT of hydrocarbon and carbon dioxide.     

On Estimating the Water Content of CO2 in Equilibrium With Formation Brine

Abstract

Water content of carbon dioxide (CO₂) is an important parameter in engineering applications, such as CO₂ storage in deep saline aquifers and enhanced oil recovery processes. A large number of experimental data have been reported on solubility of CO₂ in brine. However, experimental data on water content of CO₂ in equilibrium with formation brine are not reported in the literature. Water content of natural gas is traditionally calculated by general water content charts; however, these charts are not available for CO₂ in equilibrium with formation brine. Using an accurate fugacity and activity model available in the literature, the authors developed an efficient and simple procedure to estimate the water content of CO₂ in equilibrium with formation brine. To validate the developed procedure, the predicted CO₂-rich phase water content is compared with the reported experimental data in the literature and other predictive models for CO₂-water system. A CO₂ water content chart is presented that can be simply used to predict the water content of CO₂ in equilibrium with formation brine. The procedure presented may be used for generating necessary input data for flow simulation of CO₂ storage in deep saline aquifers.     

Interfacial Interactions Between Crude Oil and CO2 Under Reservoir Conditions

Abstract

In this paper, an experimental technique was developed to study the interfacial interactions between crude oil and CO₂ under reservoir conditions. By using the axisymmetric drop shape analysis (ADSA) for the pendant drop case, this new technique makes it possible to measure the interfacial tensions (IFTs) between crude oil and solvents, such as CO₂, at high pressures and elevated temperatures. The major component of this experimental setup is a see-through windowed high-pressure cell. In this study, the IFT of the crude-oil–CO₂ system was measured as a function of pressure at two fixed temperatures. It was found that, due to mutual interfacial interactions between crude oil and CO₂, their dynamic IFT gradually reduces to a constant value, i.e., the equilibrium IFT. The major interfacial interactions observed in this study include light-ends extraction and initial turbulent mixing. At T = 58°C, the equilibrium IFT reaches 1–2 dyne/cm when P ≥ 13.362 MPa, and only partial miscibility is achieved even up to P = 28.310 MPa. Thus, this experimental study shows that only partial miscibility can be obtained in most CO₂ flooding reservoirs. In addition, it is expected that the observed light-ends extraction and initial turbulent mixing phenomena may have significant effects on ultimate oil recovery and long-term CO₂ sequestration.     

Prediction of hydrate formation conditions to separate carbon dioxide from fuel gas mixture in the presence of various promoters

In this study estimation of hydrate formation conditions to separate carbon dioxide (CO₂) from fuel gas mixture (CO₂+H₂) was investigated in the presence of promoters such as tetra-n-butylammonium bromide (TBAB), tetra-n-butylammonium fluoride (TBAF), and tetra-n-butyl ammonium nitrate (TBANO₃). The emission of CO₂ from the combustion of fuels has been considered as the dominant contributor to global warming and environmental problems. Separation of CO₂ from fuel gas can be an effective factor to prevent many of environmental impacts. Gas hydrate process is a novel method to separate and storage some gasses. In this communication, a feed-forward artificial neural network algorithm has been developed. To develop this algorithm, the experimental data reported in the literature for hydrate formation conditions in the fuel gas system with different concentrations of promoters in aqueous phase have been used. Finally, experimental data compared with estimated data and with calculation of efficiency coefficient, mean squared error, and mean absolute error show that the experimental data and predicted data are in acceptable agreement which demonstrate the reliability of this algorithm as a predictive tool.     

Oil recovery and CO2 storage in CO2 flooding

In this study, the interfacial tension (IFT) of crude oil-carbon dioxide mixtures was measured to determine the minimum miscibility pressure. CO₂ flooding with sand packs, long cores, and heterogeneous cores was conducted to investigate the oil recovery and storage efficiency. The experiment results show that the interfacial tension decreases linearly with increasing pressure at two different pressure ranges. Under immiscible condition, the oil recovery and storage efficiency are increased by 30.1% and 52.4% when the injection pressure is increased from 13 to 22 MPa, and improved by 16.3% and 22.04% when the permeability is decreased from 270 to 10 mD, respectively. Under miscible condition, increase of injection pressure can only lead to much slower increase of oil recovery and storage efficiency, and permeability almost has no influence on oil recovery and storage efficiency. The oil recovery and storage efficiency can be remarkably reduced by heterogeneity. Water alternating CO₂ injection can improve the oil recovery and storage efficiency by 35.5% and 13.55%, respectively, compared with continuous injection.     

Thermophysical interface properties of crude oil and aqueous solution containing sulfate anions: experimental and modeling approaches

Abstract

Sulfate anion is well-known for being one of the most active agents to be injected into the oil reservoirs and being capable of not only altering the interfacial properties of crude oil but also enhancing the water solution properties in oil recovery. In the current study, the effects of temperature and pressure were studied on interfacial tension (IFT) as well as the adsorption behavior of two different solutions containing sulfate anion using experimental measurements and modeling approaches. Although it was expected that IFT values of the studied systems might decrease as temperature increased due to the improvement in the molecule mobility and solubility of crude oil in water, which consequently might lead to the reduction in its free energy, the reverse trend was observed. The measured dynamic IFT values and adsorption behavior revealed that surface excess concentration of natural surfactants (Г_NS) can be considered as the most effective parameter on interpreting IFT behavior as a function of temperature.     

Accurate estimation of CO2 adsorption on activated carbon with multi-layer feed-forward neural network (MLFNN) algorithm

Global warming due to greenhouse effect has been considered as a serious problem for many years around the world. Among the different gases which cause greenhouse gas effect, carbon dioxide is of great difficulty by entering into the surrounding atmosphere. So CO₂ capturing and separation especially by adsorption is one of the most interesting approaches because of the low equipment cost, ease of operation, simplicity of design, and low energy consumption.In this study, experimental results are presented for the adsorption equilibria of carbon dioxide on activated carbon. The adsorption equilibrium data for carbon dioxide were predicted with two commonly used isotherm models in order to compare with multi-layer feed-forward neural network (MLFNN) algorithm for a wide range of partial pressure. As a result, the ANN-based algorithm shows much better efficiency and accuracy than the Sips and Langmuir isotherms. In addition, the applicability of the Sips and Langmuir models are limited to isothermal conditions, even though the ANN-based algorithm is not restricted to the constant temperature condition. Consequently, it is proved that MLFNN algorithm is a promising model for calculation of CO₂ adsorption density on activated carbon.     

Phase equilibrium modeling of semi-clathrate hydrates of the CO2+H2/CH4/N2+TBAB aqueous solution system

In this study, the semi-clathrate hydrate dissociation pressure for the CO₂+N₂, CO₂+H₂, CO₂+CH₄, and pure CO₂ systems in the presence of different concentrations of TBAB aqueous solutions is predicted using a strong machine learning technique of multi-layer perceptron neural network (MLP-NN). The developed model, with an overall correlation coefficient (R²) of 0.9961 and mean square error (MSE) of 5.96E?02, presented an excellent accuracy in prognosticating experimental data. A complete statistical evaluation performed to promise the strength and generality of the multi-layer perceptron artificial neural network (MLP-ANN). In addition, the applicability of the proposed network and quality of experimental data was assessed through the Leverage approach.     

界面张力对相对渗透率曲线的影响

本文研究了界面张力对相对渗透率曲线的影响。为此进行了一系列非稳态驱替实验。实验是采用非胶结岩心和不同界面张力的流体体系进行的。 流体体系中,被驱动相采用的是模拟原油,驱动相分别是:地层水、表面活性剂溶液、下相微乳液和中相微孔液。研究结果表明:①界面张力对相对渗透率曲线有实质性影响。随着界面张力的降低,驱替相和被驱替相的相对渗透率曲线均升高。特别是在界面张力σ<10~(-2)mN/m时此影响更明显。②随着界面张力的降低原油采收率有明显的提高。     

Evaluation of Interfacial Tension Between Zahra Oil and ASP Solution

Alkaline-surfactant-polymer (ASP) flooding has been proved to be an effective enhanced oil recovery (EOR) method. Reduction of interfacial tension (IFT) between crude oil and ASP solution is the main mechanism in ASP flooding. Evaluating IFT between crude oil and ASP solution is a key parameter for ASP flooding in laboratory experiments or field projects. In order to obtain good result of ASP flooding in the reservoir in Zahra field, the influence of the concentration of Na₂CO₃ on IFT between Zahra crude oil and ASP solution with three different surfactants, BHJC, SS-231, and SS-233, was researched. IFT was measured with surface tension meter SVT20N, Dataphysics Co. Germany, at 72°C. For the view of IFT result anionic surfactant BHJC is more suitable for the Zahra oil field. This research is helpful for practical application of ASP flooding in Zahra oil field.     

The Equilibrium Data and Thermodynamic Modeling of Hydrate Formation for CO2 and Mixtures of CO2 and CH4 in the Presence of Methanol

Equilibrium data for the novel formation of hydrates of carbon dioxide and mixtures of carbon dioxide and methane in 20 wt% aqueous methanol solution were measured by the constant-volume method. For CO₂, these data were taken at the temperature and pressure ranges of 264.7–270.7 K and 1,470–3,160 kPa, respectively. For mixtures of carbon dioxide and methane, these data were taken at the temperature and pressure ranges of 262.9–273.7 K and 1,370–5,100 kPa, respectively. The data obtained for CO₂ in 20 wt% aqueous methanol solution were in disagreement with previously published data, but there was good agreement between our data and the predictions of thermodynamic models. The Peng-Robinson equation of state (PR EOS) coupled with the Wong-Sandler (WS) mixing rule was used to obtain the fugacities of the components in the gas and aqueous liquid phases. The PR EOS was then coupled with van der Waals-Platteeuw (vdW-P) hydrate model and applied to predict hydrate-formation conditions in the system containing methanol. The model predictions demonstrated good agreement with the experimental data.     

Evaluation of interfacial tension between ASP solution and crude oil from B oil field

The interfacial tension (IFT) between alkali-surfactant-polymer (ASP) solution and crude oil is an important parameter for evaluating the feasibility of the ASP flooding for an oil field. The IFT between six series of ASP solution and crude oil from B oil field were measured at 65°C. Each series of ASP solution was composed of NaOH or Na₂CO₃, one of the three kinds of surfactants (S1, S2, and S3), and polymer FT60. The concentration of FT60 and surfactant were 1500 and 2000 mg/L, respectively. The research results show that the IFT between ASP solution and crude oil is ultra-low in the NaOH-FT60-S2 series and NaOH-FT60-S3 series and the best concentration of NaOH is 4000 mg/L and 8000 mg/L, respectively. NaOH-FT60-S2 series is more suitable for B oil field. The IFT between ASP solution and crude oil is ultra-low in the Na₂CO₃-FT60-S2 series and the best concentration of Na₂CO₃ is 4000 mg/L.     

Utilization of Fuzzy C-means algorithm as a novel predictive tool for estimation of interfacial tension of hydrocarbon and brine

The interfacial tension that exists between brine and hydrocarbon is known as one of major properties in petroleum industries because it extremely affects oil trapping in reservoirs and consequently oil recovery. Due to aforementioned reasons the importance of investigation of this parameter has been highlighted. In the present study, Fuzzy C-means (FCM) algorithm was developed to predict interfacial tension between hydrocarbon and brine as function of different parameters such as pressure, temperature, carbon number of hydrocarbon and ionic strength of brine. The obtained results of predicting algorithm expressed its low relative error and deviation from the experimental data which gathered from the literature. Also the coefficients of determination (R²) for training and testing data were calculated 0.9508 and 0.9309 respectively. This predictive tool is simple and user friend to utilize and can be helpful for petroleum engineers to estimate interfacial tension between hydrocarbons and brine.     

Modeling Asphaltene Precipitation Under Gas Injection and Pressure Depletion Conditions

Asphaltene precipitation is a major problem during primary oil production and enhanced oil recovery in the petroleum industry. In this work, a series of experiments was carried to determine the asphaltene precipitation of bottom hole live oil during gas injection and pressure depletion condition with Iranian bottom hole live oil sample, which is close to reservoir conditions using high pressure-high temperature equilibrium cell. In the majority of previous works, the mixture of recombined oil (mixture dead oil and associated gas) was used which is far from reservoir conditions. The used pressure ranges in this work covers wide ranges from 3 to 35 MPa for natural depletion processes and 24–45 MPa for gas injection processes. Also, a new approach based on the artificial neural network (ANN) method has been developed to account the asphaltene precipitation under pressure depletion/gas injection conditions and the proposed model was verified using experimental data reported in the literature and in this work. A three-layer feed-forward ANN by using the Levenberg-Marquardt back-propagation optimization algorithm for network training has been used in proposed artificial neural network model. The maximum mean square error of 0.001191 has been found. In order to compare the performance of the proposed model based on artificial neural network method, the asphaltene precipitation experimental data under pressure depletion/gas injection conditions were correlated using Solid and Flory-Huggins models. The results show that the proposed model based on artificial neural network method predicts more accurately the asphaltene precipitation experimental data in comparison to other models with deviation of less than 5%. Also, the number of parameters required for the ANN model is less than the studied thermodynamic models. It should be noted that the Flory and solid models can correlate accurately the asphaltene precipitation during methane injection in comparison with CO₂ injection.     

The ANFIS-PSO strategy as a novel method to predict interfacial tension of hydrocarbons and brine

The interfacial tension of hydrocarbons and brine is known as one of the important parameters which are measured in petroleum and petrochemical industries for example the interfacial tension has straight effect on trapping of oil in a reservoir. In the present work the Adaptive neuro-fuzzy inference system (ANFIS) algorithm was used as a novel approach for estimation of interfacial tension between hydrocarbons and brine as function of pressure, temperature, carbon number of hydrocarbon and ionic strength of brine then the particle swarm optimization (PSO) was used to optimize the predicting model parameters.in order to better evaluation of performance of predicting algorithm the coefficient of determination (R²), average absolute relative deviation (AARD) and root mean squared error (RMSE) were estimated for different steps. The outcomes of this investigation expressed that proposed model has high potential for prediction of interfacial tension between hydrocarbons and brine.     

Prediction of Oil Recovery Factor in CO2 Injection Process

In this study, prediction of recovery factor (RF) for CO₂ injection into oil reservoirs based on artificial neural networks (ANNs) and mathematical models were investigated. To design the optimum ANN model, number of neurons, hidden layers, and training function were studied. Finally, efficiency of the models was evaluated using new data. As a result of this work, it can be concluded that it is possible to predict RF in CO₂ injection process by ANN and mathematical model. However, values that obtained from ANN were in the best agreement with the actual values than regression model. The proposed artificial neural network predicted RF during CO₂ injection with error about 0.396%.