VISCOSITY AND SOLUBILITY OF MIXTURES OF BITUMEN AND SOLVENT

Abstract

The solubilities of three bitumen samples (Suncor, Syncrude and Lloydminster) in five solvents were examined and prediction on the various bitumen-solvent mixture viscosities were made with Cragoe equation. By calculating the Cragoe constant ‘a’ for each mixture and using the average value in the Cragoe equation the prediction accuracy of the equation was improved by over 60%. Bitumen-naphtha mixtures showed the best viscosity prediction characteristics.

The solubility of the asphaltenes in the bitumen was highest in toluene among the five solvents However, naphtha, showed a moderate solvating power, which negligibly varied over the range of composition studied. Therefore naphtha, a solvent derived from bitumen was recommended as the most appropriate solvent for reducing the viscosity bitumen.     

A CORRELATION FOR VISCOSITY AND SOLVENT MASS FRACTION OF BITUMEN-DILUENT MIXTURES

A simple equation is presented for predicting the kinematic viscosity of bitumens and heavy oils mixed with diluents. The correlation has been shown to provide accurate viscosity estimates of these mixtures for a wide range of data and requires only the knowledge of the pure bitumen and pure solvent viscosities at any given temperature.

The correlation makes use of a viscosity reduction parameter which reduces error significantly when compared to similar equations presented by Chirinos et al. (1983), and Cragoe (1933). For a total of 89 data points, excluding the pure bitumens and diluents values, the correlation yielded an overall average absolute deviation of about 14 percent. The same equation was applied to predict the mass fraction of diluent required to reduce bitumen viscosity to pumping viscosity. Predicted values matched experimental values very well, with an overall average absolute deviation of about 6 percent.     

非石油基油品对渣油掺稀降黏效果及掺稀模型研究

采用水上油、页岩油和煤柴油等3种非石油基稀油对渣油进行掺稀降黏,考察了温度、稀油类型和掺稀比(稀油与调合油的质量比)对调合油黏温关系的影响,并基于实验数据,将由氩模型微分方程导出的液体黏度理论方程拓展应用到渣油掺稀调合油的黏温关系描述中,建立渣油掺稀黏度模型。结果表明:3种非石油基稀油均能够用于对渣油进行掺稀降黏,掺稀比越高,调合油黏度降低幅度越大,在掺稀比低于0.3的条件下,页岩油和水上油降黏效果较好;稀油对渣油沥青质基本片层结构的缔合作用力的影响是造成调合油黏度降低的重要原因;所建模型的计算值与实验值吻合较好,模型参数反映了调合油的分子构成,能较好地描述渣油掺稀降黏的黏温关系。     

印尼油砂溶剂抽提工艺条件的研究

对印尼油砂的组分、结构和粒径特征进行分析。分析结果表明，从印尼油砂中分离稠油沥青适用的方法是溶剂抽提工艺。考察了三氯乙烯、甲苯、石油醚、石脑油、含极性组分的石脑油5种溶剂对抽提效果的影响，确定较佳的抽提溶剂为含极性组分的石脑油。考察了印尼油砂溶剂抽提的工艺条件，推荐的最佳工艺参数为：溶剂与油砂的体积比1.3：1，抽提温度50～60 ℃，抽提时间15～30 min，搅拌速率100～300 r/min。在该条件下，稠油沥青一级抽提率达85%，二级抽提率达93%。     

The Solubility of Asphaltenes in Different Hydrocarbon Liquids

Abstract

Bitumen miscibility in low-molecular-weight hydrocarbon liquids was evaluated. The presence of toluene in bitumen–solvent blends improved bitumen miscibility and led to the delay in onset of asphaltenes precipitation due to the dipole–dipole and heteromolecular interactions. For heavy naphtha, the results showed reduced asphaltenes precipitation and enthalpy of mixing due to strong hydrogen bonding and moderate homomolecular interactions. Hexane and light naphtha systems showed higher heats of mixing and higher asphaltenes precipitation due to homomolecular interactions. The best miscibility characteristics of heavy petroleum were obtained with aromatic solvents and solvent mixtures that contained increasing composition of toluene.     

对氨基-3-硝基苯磺酸合成中溶剂的选择

以煤油、溶剂汽油、邻二氯苯分别作为单组分溶剂 ,又用石油醚分别与上述溶剂按一定比例配制成三种混合溶剂 ,将这六种溶剂分别用于对氨基 3硝基苯磺酸的合成。结果证明 ,混合溶剂比单组分溶剂效果好 ,用溶剂汽油与石油醚配成的混合溶剂最好 ,产品收率大于 91 % ,溶剂回收率大于 95%。     

FCC汽油选择性加氢脱硫过程中烃类组成与辛烷值的关系

 以典型的FCC汽油重馏分为原料,在Co-Mo/Al2O3催化剂上进行选择性加氢脱硫试验。结果表明,烯烃加氢饱和是导致汽油加氢后辛烷值下降的主要原因。按照烯烃加氢后辛烷值损失的大小,将其分成五类。五类烯烃加氢饱和后辛烷值损失由大到小的顺序是：直链内烯烃>直链端烯烃>C7+单支链烯烃>环烯烃>多支链和C5,C6支链烯烃。建立了烯烃变化量与辛烷值损失量的关联式,可以预测加氢汽油辛烷值的损失。     

Application of Grid partitioning based Fuzzy inference system and ANFIS as novel approach for modeling of Athabasca bitumen and tetradecane mixture viscosity

The heavy oil and bitumen reservoirs have effective role on supplying energy due to their availability in the world. The bitumen has extremely high viscosity so this type of reservoirs has numerous problems in production and trans- portation.one of the common approach for reduction of viscosity is injection of solvents such as tetradecane. In the present study the Grid partitioning based Fuzzy inference system was coupled with ANFIS to propose a novel algorithm for prediction of bitumen and tetradecane mixture viscosity in terms of pressure, temperature and weight fraction of the tetradecane. In the present study, the coefficients of determination for training and testing phases are determined as 0.9819 and 0.9525 respectively and the models are visualized and compared with experimental data in literature. According to the results the predicting method has acceptable accuracy for prediction of bitumen and tetradecane mixture viscosity.     

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.     

重质船用燃料油黏度预测模型研究

研究了现有黏度预测模型应用于重质船用燃料油黏度预测的可行性,筛选几种常见的黏度物理模型,进行试验数据对比和最优模型选取,基于重质船用燃料油数据库对Cragoe模型进行修正,并结合掺稀降黏试验数据分析混合机制对预测模型相对误差的影响。结果表明,针对目前市场上常用的重质船用燃料油调合组分,采用Cragoe黏度模型进行预测误差较小。这是由于Cragoe黏度模型的预测不受组分油黏度比的限制,在重质船用燃料油中的适用性最好。采用所提出的修正模型,可进一步降低对重质船用燃料油黏度预测的误差。分析多组分调合的结果显示,若组分中的黏度呈梯度分布时可降低预测误差。另外,渣油与稀组分油（简称稀油）调合时,沥青质的络合效应在一定程度上会影响模型的预测准确性。     

VISCOSITY ESTIMATION FOR BITUMEN-DILUENT MIXTURES

A generalized viscosity correlation has been used to predict the viscosity-temperature relationship of bitumen mixed with various proportions of diluents such as GCOS synthetic crude, mobil solvent and naphtha. The results showed that the correlation can accurately predict the viscosities of bitumen-diluent mixtures based on a single viscosity measurement at 30°C and one atmosphere. For 300 experimental points, the correlation yielded an overall average absolute deviation between predicted and experimental values of 8.7%. It is found that the correlation gave high percent errors for mixtures with high disparity in viscosity between bitumen and diluent.     

DISTRIBUTION AND TYPES OF SOLIDS ASSOCIATED WITH BITUMEN

In the conventional Hot Water Extraction Process bitumen is separated as a froth that is then diluted with naphtha and subjected to two stages of centrifugation. The resulting bitumen solution still contains residual water, dissolved salts and mineral solids. Before upgrading the solvent and other volatile components are removed by topping at 524°C. The salts and mineral solids remain with topped bitumen; their presence can lead to serious operational problems in the bitumen upgrading process. In the present work the solids associated with bitumen (BS) have been identified as mainly ultra-fine (nano sized) aluminosilicate clays coated with strongly bound toluene insoluble organic material having “asphaitene characteristics”. It is proposed that these ultra-fine clays with their strong tendency to collect at oil-water interfaces, are the key component responsible for the presence of intractable water and associated salts in bitumen froth.     

EXTRACTION OF HEAVY OIL AND BITUMEN USING VAPORIZED HYDROCARBON SOLVENTS

The huge natural resources present in the form of heavy oil and bitumen may be the principal source of fuel for human civilization in the coming century. However, due to very high viscosities of these crudes the use of conventional recovery techniques is not feasible. The viscosity decreases drastically with increase in temperature; this principle is utilized in thermal recovery processes like SAGD. Using long horizontal wells, very high production rates can be achieved in this process. However, steam processes suffer from energy inefficiency largely due to heat losses to the cap and base rock. This can be avoided if instead of steam, solvent is used to reduce viscosity.

In the proposed process, “Vapex”, vaporized hydrocarbon solvents are used to extract heavy oil and bitumen using the concept of gravity drainage. Hydrocarbon solvents, when present in sufficient concentration, causes deasphalting of these crudes. Although deasphalting improves the quality of the produced crude, the possibility of reservoir plugging due to asphaltene deposition is an important concern for using this process Experiments carried out in Hele-Shaw cells indicated higher extraction rates with deasphalting compared to that without deasphalting leading to the conclusion that asphaltene deposition may not be a constraint for the process. This was confirmed in experiments carried out in scaled packed model. The experimental results showed that the flow rates achieved in the porous media are higher than those obtained by scaling up the results of Hele-Shaw cell experiments carried.out under identical conditions of temperature and pressure. This is due to the extended interfacial contact, capillary imbibition, surface renewal and transient mass transfer at the interface. Production rates predicted from these experimental results are attractive for field operations     

Modeling of viscosity for mixtures of Athabasca bitumen and heavy n-alkane with LSSVM algorithm

The resources of heavy oil and bitumen are more than those of conventional light crude oil in the world. Diluting the bitumen with liquid solvent can decrease viscosity and increase the empty space between molecules. Tetradecane is a candidate as liquid solvent to dilute the bitumen. Owning to the sensitivity of enhanced oil recovery process, the accurate approximation for the viscosity of aforementioned mixture is important to decrease uncertainty. The aim of this study was to develop an effective relation between the viscosity of Athabasca bitumen and heavy n-alkane mixtures based on temperature, pressure, and weight percentage of n-tetradecane using the least square support vector machine. This computational model was compared with the previous developed correlation and its accuracy was confirmed. The value of R² and MSE obtained 1.00 and 1.02 for this model, respectively. This developed predictive tool can be applied as an accurate estimation for any mixture of heavy oil with liquid solvent.     

神经网络构建稠油掺稀黏度预测模型研究

本文利用BP神经网络能够较好地在实验数据基础上建立稠油掺稀黏度预测模型,以对新疆塔河油田稠油掺入四种稀油的黏度预测为例,通过BP神经网络建立预测模型,并与四种传统基于线性回归的建模方法及进行改进的方法进行对比,结果表明：利用神经网络建立模型的最大误差为4.1%,黏度与温度、稀稠比的非线性关系能够较好拟合,对比基于线性回归方法的建模方法及其改进算法有着更高的拟合精度。     

Evolving ANFIS model to estimate density of bitumen-tetradecane mixtures

Recent investigations have proved more worldwide availability of heavy crude oil resources such as bitumen than those with conventional crude oil. Diluting the bitumen through injection of solvents including tetradecane into such reservoirs to decrease the density and viscosity of bitumen has been found to be an efficient enhanced oil recovery approach. This study focuses on introducing an effective and robust density predictive method for Athabasca bitumen-tetradecane mixtures against pressure, temperature and solvent weight percent through implementation of adaptive neuro-fuzzy interference system technique. The emerged results of proposed model were compared to experimentally reported and correlation-based density values in different conditions. Values of 0.003805 and 1.00 were achieved for mean square error and R², respectively. The developed model is therefore regarded as a highly appropriate tool for the purpose of bitumen-tetradecane mixture density estimation.     

硬质道路石油沥青关键组分的研制

筛选出适合制备硬质道路石油沥青关键组分的减压渣油，并以此为原料，考察了蒸馏深拔、深度氧化及溶剂脱沥青工艺制备的硬质道路沥青关键组分的性能。所制备的关键组分能够大幅提升基质沥青的高温性能，且通用性好；将关键组分与基质沥青按适合比例调合可以稳定生产高温性能好的硬质道路石油沥青，其混合料耐高温性能好。同时,为便于硬质沥青关键组分的使用,还开发了关键组分的造粒成型技术,提出其适于成型造粒的黏度范围为0.6～6 Pa?S。采用关键组分生产的硬质道路沥青用于高速公路实体工程，经过2年以上的气候和通车考验，沥青路面无车辙、无开裂，保持优良的使用性能。     

PSO-ANFIS modeling of viscosity for mixtures of Athabasca bitumen and a high-boiling n-alkane

The bitumen and heavy oil reservoirs are more in number than light crude oil reservoirs in the world. To increase the empty space between molecules and decrease viscosity, the bitumen was diluted with a liquid solvent such as tetradecane. Due to the sensitivity of enhanced oil recovery process, the accurate approximation for the viscosity of mentioned mixture is important. The purpose of this study was to develop an effective relation between the viscosity of Athabasca bitumen and heavy n-alkane mixtures based on pressure, temperature, and the weight percentage of n-tetradecane using the adaptive neuro-fuzzy inference system method. For this model, the value of MRE and R² was obtained as 0.34% and 1.00, respectively; so this model can be applied as an accurate approximation for any mixture of heavy oil with a liquid solvent.     

裂解乙烯副产C9馏分制备芳烃溶剂油及石油树脂

利用沸程在100～200 ℃裂解乙烯的副产C9馏分为原料，研究了以Lewis酸为催化剂的两段聚合法制备芳烃溶剂油的工艺条件。主要考察催化剂用量、聚合温度、聚合时间对产品分布的影响。结果表明，采用两段聚合法，在催化剂三氟化硼乙醚络合质量分数1.0%、三氯化铝质量分数1.5%、聚合温度60℃、聚合时间6 h的条件下，芳烃溶剂油收率为71.1%，石油树脂收率最高可达20%；同时所得的芳烃溶剂油切割为120号和200号，指标达到行业标准。     

Estimating n-tetradecane/bitumen mixture viscosity in solvent-assisted oil recovery process using GEP and GMDH modeling approaches

In this study, the methods of group method of data handling (GMDH) and gene expression programming (GEP) were used to develop symbolic correlations for truthful viscosity estimation of n-tetradecane/bitumen mixture. A number of graphical and statistical tools were utilized to make evident the omnipotence of the proposed models as compared to the published literature correlations. It is found that the GMDH model is the best acting approach with the lowest total error of 2.51% and the highest R² of 0.9994. Sensitivity analysis verifies that concentration of the solvent is the most influencing input parameter on the viscosity estimation of n-tetradecane/bitumen mixture.