The effect of different solvents on the density of undersaturated athabasca bitumen: Application in VAPEX and ES-SAGD

Predicting the density of bitumen after solvent injection is highly required in solvent-based recovery techniques like expanding solvent-steam assisted gravity drainage (ES-SAGD) and vapor extraction (VAPEX) in order to estimate the cumulative oil recovery by these processes. Using experimental procedures for this purpose is so expensive and time-consuming; therefore, it is crucial to propose a rapid and accurate model for predicting the effect of various solvents on the dilution of bitumen. In this study, an adaptive neuro-fuzzy interference system is introduced to estimate the effect of methane, ethane, propane, butane, carbon dioxide, and n-hexane on the density of undersaturated Athabasca bitumen in wide ranges of operating conditions. The obtained results were in an excellent agreement with experimental data with coefficients of determination (R²) of 0.99997 and 0.99948 for training and testing datasets, respectively. Statistical analyses illustrate the superiority of the proposed model in predicting the bitumen density at different conditions.     

Adsorption kinetics of natural surfactants on carbonate minerals in enhanced oil recovery processes

One of the most promising methods for improving oil recovery from carbonate reservoirs is surfactant flooding in which the trapped oil can be mobilized by alteration in the wettability of rock surfaces and also reduction in the interfacial tension between oil and water. Adsorption of surfactants on carbonate minerals plays a key role in designing this process and may make it less effective for enhancing oil recovery. Natural surfactants have been proposed by many researchers since they have lower cost and also less detrimental environmental effects compared to the industrial surfactants. Well-established predictive models for predicting the adsorption of natural surfactants have some issues which need to be addressed. Therefore, developing an accurate, rapid and simple model is crucial. In this study, a least square support vector machine (LSSVM) optimized with coupled simulated annealing (CSA) algorithm is developed for accurate prediction of natural surfactants kinetic adsorption on carbonate minerals. Obtained results by this model were in a very good agreement with experimental results. Additionally, the results showed that the proposed model has the highest accuracy and performance in comparison to the previous kinetic models. Afterward, the effect of natural surfactants adsorption on the amount of oil recovery and also the quality of the produced oil was investigated via core flooding tests for showing the importance of determining the adsorption of surfactants before any surfactant flooding. Results demonstrated that lower surfactants adsorption yields higher oil recovery factor and oil with higher viscosity.     

Using biosurfactant producing bacteria isolated from an Iranian oil field for application in microbial enhanced oil recovery

Microbial enhanced oil recovery (MEOR) is a useful technique to improve oil recovery from depleted oil reservoirs beyond primary and secondary recovery operations using bacteria and their metabolites. In the present study, the biosurfactant production potential of Bacillus licheniformis microorganisms that were isolated from oil samples of Zilaei reservoir in the southwest of Iran was explored under extreme conditions. Growth media with different temperatures of 40, 50, 60, and 70°C; salinities of 1, 3, 5, and 7 wt%; and yeast extract concentrations of 0.5, 1, 1.5, and 2 g/L were used to find the optimum growth conditions. The results demonstrated that bacteria grown in a mineral salt solution with temperature of 50°C, salinity of 1 wt% and yeast extract concentration of 1 g/L has the highest growth rate and therefore, these conditions are the optimum conditions for growing the introduced bacterium. This isolate was selected as the higher biosurfactant producer. The obtained biosurfactants by bacteria isolated in a medium with these conditions could reduce the interfacial tension of crude oil/water system from 36.8 to 0.93 mN/m and surface tension of water from 72 to 23.8 mN/m. The results of the core flooding tests showed that the tertiary oil recovery efficiency due to the injection of microorganisms was 13.7% of original oil in place and bacteria could reduce the oil viscosity by 41.242% at optimum conditions. Based on these results, the isolated microorganism is a promising candidate for the development of microbial oil recovery processes.     

Adsorption of natural surfactants on sandstone rocks: Effect on the quality of the recovered asphaltenic oil

Natural surfactants are important for any enhancing oil recovery applications since these kinds of surfactants have less detrimental effects and also lower cost in comparison to the industrial surfactants. Adsorption of surfactants on sandstone minerals is an important issue which needs to be considered before applying any surfactant-based enhanced oil recovery technique. In this study, a new model based on adaptive neuro-fuzzy interference system was developed in order to simulate the kinetic behavior of natural surfactants adsorption on sandstone minerals with high accuracy. Performance of the model was investigated by comparing the results of the proposed model with the results of previous well-known kinetic models. Results demonstrated that this model has the highest accuracy compared to the other well-established models found in literature. Finally, in order to show the importance of modeling surfactants adsorption, the effect of a natural surfactant on the quality of the produced oil was investigated by performing SARA analysis on the recovered oil at various conditions of surfactant adsorption. It is shown that in the case of lower surfactant adsorption on sandstone minerals, the produced oil has higher asphaltene fractions.     

Prediction of dynamic viscosity of n-alkanes at high pressures using a rigorous approach

An adaptive neuro-fuzzy interference system has been developed for estimating the dynamic viscosity of n-alkanes in a wide range of operating conditions. In this study, for the first time, a simple predictive model is proposed for viscosity prediction of n-pentane, n-octane, n-nonane, n-decane and dodecane at various pressures and temperatures, especially at high pressures, without needing to measurement or estimation of density. This tool predicts the dynamic viscosity of the n-alkanes as function of pressure, temperature and n-alkanes' molecular weight. The obtained results of the model were in an excellent agreement with experimental data with an acceptable coefficient of determination of 0.999 for both training and testing datasets. Moreover, the validity of the proposed model for viscosity trends prediction at various conditions was demonstrated and it showed a very good match with actual data. This model is simple to use and can be of massive evaluation for better understanding the behavior of fluids under reservoir conditions.     

基于瞬时产量递减分析确定天然裂缝性油藏平均地层压力

以Warren和Root等提出的裂缝-孔隙双重介质模型为基础,拓展延伸了Mavor、Cinco-Ley和Daprat等的裂缝系统拟稳态-定产量生产的解及基质系统拟稳态-定压力生产的解,提出了根据天然裂缝性油藏定压开采的瞬时产量递减准确估算油藏平均压力的新方法。具体方法为:分别将基质产量方程和裂缝产量方程两端取自然对数,得到两条直线;再利用其斜率和截距确定弹性储容比、油藏总储容以及泄油面积,最后确定平均地层压力。经算例和实例验证,新方法简单实用,与物质平衡法和Tiab直接合成技术法得到的估算结果十分接近。     

Connectionist approaches for solubility prediction of n-alkanes in supercritical carbon dioxide

Carbon dioxide injection is a known promising and economical technology for improving oil recovery. Despite its immense effect on oil recovery, the application of this technique in modern recovery industry has been limited due to poor solubility of n-alkanes in supercritical CO₂. Therefore, it is very consequential to investigate the solubility of different n-alkanes in supercritical CO₂. Since experimental methods for measuring the solubility of n-alkanes in supercritical CO₂ at different temperatures and pressures are not economical and usually take a long time, feasibility of applying intelligent tools in the solubility prediction of different n-alkanes in supercritical CO₂ at pressures up to 45.9 MPa was conducted in this study. For this purpose, two models including an artificial neural network and an adaptive neuro-fuzzy interference system (ANFIS) both trained with particle swarm optimization (PSO) algorithm were used for simulating this process. Calculated mole fractions of n-alkanes in supercritical CO₂ from ANFIS–PSO model were excellently consistent with actual measured values. Moreover, comparison between these models and Chrastil semiempirical correlation show superiority and accuracy of the proposed ANFIS–PSO approach. Results of this study indicate that ANFIS–PSO method is a powerful technique for predicting solubility of n-alkanes in supercritical CO₂.

     

Simulation Studies on Growth and Death of Microorganisms Using the Oil‐Degrading Bacteria Petrotoga sp.

Three models were developed to estimate the potential of the selected bacteria Petrotoga sp., a thermophilic anaerobic oil‐degrading microorganism. Fourteen data sets of these bacteria were simulated by a multilayer feed‐forward neural network and an adaptive neuro‐fuzzy interference system. Twelve data sets served for training and two for testing these models. A simplified numerical model was performed assuming two phases in the growth process of oil‐degrading microorganisms, the logarithmic growth phase and the death phase. Comparison between these models in predicting bacterial cell concentration for different data sets indicates little difference between the overall average relative errors of the three methods and that all can be applied for prediction. Effects of salinity concentration, amount of yeast extract, and temperature on bacterial cell concentration were simulated by numerical and neural network models.