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.     

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.     

Applying Grid partitioning based Fuzzy inference system method to estimate interfacial tension of brine and hydrocarbon

The oil recovery reservoirs and oil trapping in the reservoirs are extensively function of interfacial tension between brine and hydrocarbon, so estimation of interfacial tension becomes one of the interesting topics in petroleum industry. In this study, Grid partitioning based Fuzzy inference system method is utilized to forecast interfacial tension of hydrocarbon and brine based on various effective parameters such as ionic strength of brine, carbon number of hydrocarbon, pressure, and temperature. The estimated values of interfacial tension were compared with real interfacial tension of brine and hydrocarbon using graphical and statistical analyses. The determined coefficients of determination (R²) for training and testing phases were 0.9916 and 0.9447, respectively. The comparing analyses express that the Grid partitioning based Fuzzy inference system method has great ability in prediction of interfacial tension, and it can be used as an applicable tool in petroleum industry.     

On prediction of asphaltene precipitation in different operational conditions utilization of LSSVM algorithm

Asphaltene which is known as one of the fractions of oil, can cause the important problems during production of crude oil in reservoir, tubing and surface facilities so these problems can influence the production cost and time. In order to predicting and solving asphaltene problems, a powerful Least squares support vector machine (LSSVM) algorithm were developed for asphaltene precipitation estimation as function of dilution ratio, temperature, precipitant carbon number, asphaltene content and API of oil. A total number of 428 measured data were utilized to train and test of LSSVM algorithm. The average absolute relative deviation (AARD), the coefficient of determination (R²) and root mean square error (RMSE) were determined as 7.7569, 0.98552 and 0.26312 respectively. Based on these statistical parameters and graphical analysis it can be concluded that the predicting algorithm has enough reliability and accuracy in prediction of asphaltene precipitation.     

Application of LSSVM algorithm as a novel tool for prediction of density of bitumen and heavy n-alkane mixture

The most of oil reservoirs in the world are heavy oil and bitumen reservoirs. Due to high viscosity and density of these types of reservoirs the production has problems so importance of enhanced oil recovery (EOR) processes for them is clear. The injection of solvents such as tetradecane is known as one of methods which improve oil recovery from bitumen reservoirs. In the present investigation, the Least squares support vector machine (LSSVM) algorithm was used to estimate density of Athabasca bitumen and heavy n-alkane mixture in term of temperature, pressure and weight percent of the solvent. The Root mean square error (RMSE), average absolute relative deviation (AARD) and the coefficient of determination (R²) for total dataset are determined 0.033466, 0.0025686 and 1 respectively. The predicted results indicate that the LSSVM algorithm has potential to be a predicting machine for the bitumen-heavy alkane mixture density prediction.     

用动界面张力方法研究原油/盐水界面的性质Ⅰ.原油天然表面活性剂和破乳剂的竞争吸附现象

测定了原油 /盐水的动界面张力 ,比较了在无破乳剂存在时几种体系的动界面张力曲线。证明了原油天然表面活性剂分子渐渐扩散吸附到原油 /盐水界面上 ,当体系中有破乳剂存在时 ,破乳剂分子以竞争的方式扩散吸附到原油 /盐水界面上。用动界面张力方法研究原油天然表面活性剂和破乳剂在原油 /盐水界面的吸附速率对研究原油乳状液的破乳机理和优化破乳条件十分重要。     

Applying of LSSVM approach as a novel tool for accurate prediction of asphaltene inhibition efficiency

Asphaltene precipitation is one of critical problems for petroleum industries. There are different methods for inhibition of asphaltene precipitation. One of the common and effective methods for inhibition of asphaltene precipitation is utilizing asphaltene inhibitors. In this work, Least squares support vector machine (LSSVM) algorithm was coupled with simplex optimizer to create a novel and accurate tool for estimation of effect of inhibitors on asphaltene precipitation as function of concentration and structure of inhibitors and crude oil properties. To this end a total number of 75 measured data was extracted from the literature for training and testing of predicting model. The average absolute relative deviation (AARD), the coefficient of determination (R²) and root mean square error (RMSE) of total data for prediction algorithm were determined as 1.1479, 0.99406 and 0.61039. According to these parameters and graphical comparisons the LSSVM algorithm has potential to predict asphaltene precipitation in high degree of accuracy.     

低渗透油藏自发渗吸驱油实验研究

通过低渗透亲水岩心自发渗吸实验,探讨了低渗透油藏不同界面张力体系的渗吸驱油过程。研究结果表明,注入水渗吸体系因毛细管力较高、毛细管力与重力比值较大,其渗吸过程为毛细管力支配下的逆向渗吸。与注入水渗吸结果相比,化学剂溶液因降低油水界面张力,在孔隙介质中能使更多的原油参与渗流过程,使更多的剩余油变为可动油,提高了渗吸平衡时的原油采出程度,因而提高了低渗透油藏原油的采收率。     

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.     

缝洞型油藏剩余油形成机制及分布规律

采用钻孔和熔蜡的方法分别制备了缝洞油藏定量模型和随机模型,并用两种模型进行全直径岩心驱油实验,研究缝洞型油藏中剩余油形成机制及分布规律。研究结果表明:驱替相与被驱替相的密度差异和缝洞连接点位置决定溶洞中流体分布规律;一次水驱结束后剩余油包括油水密度差异形成的"阁楼油"、油水置换非瞬时性造成的"封存油"、溶洞不规则性造成的"角隅油"、缝洞复杂连接关系或者低连通度造成的"盲洞油"以及岩石润湿性造成的"油膜"。连接点位置越高,"阁楼油"含量越少;注入速度越低、原油黏度越小,"封存油"含量越少;溶洞形状越规则,角隅数量越少,"角隅油"含量越少;缝洞连接关系越简单、连通程度越高,"盲洞油"含量越少;水湿岩石表面"油膜"含量较少。     

表面活性剂水溶液与油之间的动态界面张力研究

本文研究了两种表面活性剂(TRS10-80和天津ABS)盐水溶液与煤油之间的界面张力特性,提出了表面活性剂盐水溶液/煤油体系中动态界面张力产生的机理,讨论了表面活性剂对油的增溶作用对界面张力的影响。经过长期预平衡的表面活性剂盐水溶液/煤油体系仍然存在明显的功态界面张力特性。     

新型表面活性聚合物驱油剂

从分子结构与性能关系入手,研发了具有低界面张力、可聚合的表面活性单体,将可聚合表面活性单体与丙烯酰胺进行共聚,采用复合引发体系、控制低温聚合的方法合成了一种新型表面活性聚合物驱油剂,并利用红外光谱、冷冻蚀刻电镜技术对其结构及其在溶液中的分布状态进行了表征。研究结果表明,可聚合表面活性单体与丙烯酰胺参与了接枝共聚,因而克服了色谱分离效应,新型表面活性聚合物驱油剂在不同水质中具有良好的水溶性、增黏性、耐温抗盐性与抗剪切性能,同时具有较低的油水界面张力,质量分数为0.15%的聚合物溶液与大庆采油一厂原油的界面张力达到1×10 1mN/m数量级。岩心驱油实验表明:新型表面活性聚合物驱油剂具有较好的增黏性及较低的油水界面张力,采收率较普通水解聚丙烯酰胺提高了5.2%。     

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.     

An Efficient Approach for the Determination of Oil Production Rate During the Water-flooding Recovery Method

In this work, a mathematical methodology namely, least square support vector machine (LSSVM) is implemented to predict the variation of oil production rate as a function of oil water viscosity ratio and water injection rate for water-flooding. Furthermore, the coupled simulated annealing (CSA) optimization technique is coupled with LSSVM to find the optimal architecture and parameters of the LSSVM. The obtained results demonstrate that the CSA-LSSVM estimations are in a satisfactory agreement with literature-reported data and the previously published correlation. Consequently, the R² and average absolute relative deviation of CSA-LSSVM model in testing phase are reported 0.979 and 8.15, respectively.     

Estimation of water-hydrocarbon mutual solubility in gas processing operations using an intelligent model

An accurate prediction of the mutual solubilities of hydrocarbons and water is extremely useful in oil, gas, and chemical industries. Estimating the solubility of hydrocarbons in water is required to describe their phase distribution through the removal process and also in the design of separation equipment. The current study plays emphasis on applying the predictive model based on the least square support vector machine (LSSVM) to estimate mutual water-hydrocarbon solubility at a wide range of conditions. A genetic algorithm (GA) was employed to choose and optimize hyperparameters (γ and σ²), which are embedded in LSSVM model. Utilization of this model showed high competence of the applied model in terms of coefficient of determination (R²) of 0.9998 and 0.9994, Average absolute relative deviation (AARD) of 1.1378 and 1.12459 from experimental values for predicted water solubility in hydrocarbons and hydrocarbon solubility in water, correspondingly. Using this method is quite simple and accurate to determine the mutual water-hydrocarbon solubility with negligible uncertainty.     

Synthesis and Interfacial Behavior of Decyl Methylnaphthalene Sulfonate

Abstract

High purity decyl methylnaphthalene sulfonate (DMNS) surfactant was synthesized. The purity of product was determined by HPLC, and the structure was confirmed by IR, UV, and ESI-MS. The surface and oil-water interfacial activities of DMNS surfactant were studied. The effects of concentrations of the surfactant, alkali, and inorganic salt on the dynamic interfacial behavior of crude oil/Shengli Oil Field/surfactant oil flooding systems were studied, and comparitive studies of systems with strong and buffered alkali were also carried out. Results showed that DMNS surfactant possessed great capability and efficiency for lowering solution surface tension and oil-water interfacial tension. The critical micelle concentration (cmc) was 0.02% and the surface tension at this concentration was 31.61 mN.m^?1. At proper concentrations of the surfactant, alkali, and inorganic salt, the dynamic interfacial tension between the crude oil of the Shengli Oil Field and the surfactant oil flooding system reached a minimum value of 10^?5–10^?6 mN.m^?1 in a very short time (3–20 min) and maintained an ultra-low value (< 10^?2 mN.m^?1) for a long period of time (15–87 min). The crude oil/surfactant systems presented satisfactory interfacial behavior. DMNS has great potential to be used in enhanced oil recovery (EOR) with low cost and high efficiency.     

Accurate prediction of properties of carbon dioxide for carbon capture and sequestration operations

Development of robust predictive models to estimate the transport properties of gases (namely viscosity and thermal conductivity) is of immense help in many engineering applications. This study highlights the application of the artificial neural network (ANN) and least squares support vector machine (LSSVM) modeling approaches to estimate the viscosity and thermal conductivity of CO₂. To propose the machine learning methods, a total of 800 data gathered from the literature covering a wide temperature range of 200–1000 K and a wide pressure range of 0.1–100 MPa were used. Particle swarm optimization (PSO) and genetic algorithm (GA) as population-based stochastic search algorithms were applied for training of ANNs and to achieve the optimum LSSVM model variables. For the purpose of predicting viscosity, the PSO-ANN and GA-LSSVM methods yielded the mean absolute error (MAE) and coefficient of determination (R²) values of 1.736 and 0.995 as well as 0.51930 and 0.99934, respectively for the whole data set, while for the purpose of predicting thermal conductivity, the PSO-ANN and GA-LSSVM models yielded the MAE and R² values of 1.43044 and 0.99704 as well as 0.72140 and 0.99857, respectively for the whole data set. Both methods provide properly capable method for predicting the thermal conductivity and viscosity of CO₂.     

Dissociation of polar oil components in low salinity water and its impact on crude oil–brine interfacial interactions and physical properties

Despite many efforts into the study of fluids interaction in low salinity water flooding, they are not probing the basics of transport phenomena between the involved phases. This work is aimed to bring new understanding of fluid–fluid interaction during low salinity water flooding through a series of organized experiments in which a crude oil sample with known properties was kept in contact with different brine solutions of various ionic strengths. Measuring brine pH, conductivity and crude oil viscosity and density for a period of 45 days illustrates the strong effect of the contact time and ionic strength on the dissociation of polar components and physical properties of the crude oil and brine. Besides, the interfacial tension (IFT) measurements show that the interfacial interactions are affected by several competitive interfacial processes. By decreasing the ionic strength of the brine, the solubility of naphthenic acids in the aqueous solution increases, and hence, the conductivity and the pH of the aqueous phase decrease. To verify this important finding, UV–Vis spectroscopy and 1H NMR analysis were also performed on aged brine samples. Notably, there is an ionic strength of brine in which the lowest IFT is observed, while the other physical properties are remained relatively unchanged.     

Petroleum Sulfonates as Oil Displacement Agents and Application to Gudao Oil Field

Abstract

In terms of the condition of injection of water after polymer flooding of the Gudao oil field (Shengli, China), the following water quickly broke though the bank to the production wells, while most of residual oil remained in the formation. To solve the problem, two types of petroleum sulfonates made in China were selected to form an oil displacement agent (ODA) solution. The petroleum sulfonate available for crude oil from the Gudao oil field with the ultra-low interfacial tension can be found by drawing an oil/water interfacial tension contour diagram. The results show that the interfacial tension can be lower than 3.6 × 10^?4 mN/m when the active agent is contained with 0.25% Kelamayi petroleum sulfonate (KPS) + 0.225% Anqing petroleum sulfonate (APS) and if the agent reduces water resistance by entering the tiny pore to improve sweep coefficient and oil displacement efficiency. The polymer has no influence on the balanced value of interfacial tension but delays the interfacial tension to reach the balance. Pouring 0.3 pore volume (PV) high-efficient ODA into reservoir can improve 17% oil recovery (OR). Synergistic effects of two types of petroleum sulfonate with low cost to enhance OR will have an excellent prospect for enhanced oil recovery (EOR).     

Modeling of CO2-brine interfacial tension: Application to enhanced oil recovery

Development of reliable and accurate models to estimate carbon dioxide–brine interfacial tension (IFT) is necessary, since its experimental measurement is time-consuming and requires expensive experimental apparatus as well as complicated interpretation procedure. In the current study, feed forward artificial neural network is used for estimation of CO₂–brine IFT based on data from published literature which consists of a number of carbon dioxide–brine interfacial tension data covering broad ranges of temperature, total salinity, mole fractions of impure components and pressure. Trial-and-error method is utilized to optimize the artificial neural network topology in order to enhance its capability of generalization. The results showed that there is good agreement between experimental values and modeling results. Comparison of the empirical correlations with the proposed model suggests that the current model can predict the CO₂–brine IFT more accurately and robustly.