Modeling of Coprecipitation of Resin and Asphaltene in Crude Oil by Association Equation of State

Abstract

Resin content is an effective parameter that has adverse effect on precipitation of asphaltene in crude oil. Fluctuations in temperature, pressure, or oil composition disturb the chemical equilibrium in a reservoir, which results in coprecipitation of resin and asphaltene. In this work, coprecipitation of resin and asphaltene has been modeled using an association equation of state (AEOS) in which asphaltene and resin are considered associate components of oil. According to association fluid theory, the total compressibility factor is assumed to be the sum of physical and chemical compressibility factors. Liquid–liquid and liquid–vapor equilibrium calculations are accomplished with the assumption that asphaltene and resin do not contribute in the vapor phase. Comparison of experimental asphaltene precipitation with that obtained from the model developed proves the acceptability of the proposed model.     

A new correlation for vapor pressure prediction of natural gas mixture

In this study, the Antoine equation of vapor pressure prediction for pure substances has been extended to predict vapor pressure values of a natural gas mixture at a temperature range of 125–230 K. The correlation was modified by applying the software MATLAB 8.2 R2013b (The MathWorks, Natick, MA). The developed correlation for multicomponent natural gas mixture is predictable with few input data of critical temperature, critical pressure, and the acentric factor. For the examined natural gas mixture, the predicted vapor pressures show comparable values with those obtained by applying well-known and accurate cubical equation of state (EOS) of Soave–Redlich–Kwong (SRK) and Peng–Robinson (PR) for the whole range of studied temperatures. From the results, an average absolute deviation of 1.735% for the developed correlation in this work was obtained from the actual results calculated experimentally, which is lower than that obtained by SRK EOS and close to results of PR EOS.     

A Parametric Methodology in Tuning the Peng-Robinson (PR) Equation of State for Gas Condensate Systems

A tuned equation of state (EOS) is used in reservoir engineering for the evaluation of gas and condensate reserves, desired production methods, and facilities for field development. Publications show that the two most widely used sets of parameters from the EOS that are tuned are the binary interaction coefficient (BIC) with the critical properties and acentric factor or BIC and the constants called the omegas (Ωs). A volume shift parameter (VSP) can also be used in cubic EOS as a tuning parameter to correct for the prediction of liquid density. However, the open literature does not demonstrate if the VSP could be used with one or both parameter sets. In this study, the Peng-Robinson EOS was tuned and tested to predict constant volume depletion (CVD) data for six gas condensate samples. The two sets of tuning parameters were used with and without the VSP. Our parametric study demonstrated that the VSP should not be applied with the Ωs when tuning the Peng-Robinson EOS. With weight factors of 1 for liquid volume and 10 for gas compressibility, without the VSP, the Ωs give better prediction of CVD data than the critical properties and acentric factor even with the VSP included. This tuning technique with one regression step showed consistency in tuning the Peng-Robinson EOS with the Ωs and could be used for simulation studies of gas condensate systems.     

A New Investigation on Asphaltene Precipitation: Experimental and a New Thermodynamic Approach

The process of asphaltene precipitation has a substantial effect on oil production in petroleum industry. In this work, a series of experiments was carried to determine the asphaltene precipitation of crude oil during solvent titration condition with Iranian crude oil sample using experimental set up. The precipitants such as pentane, heptanes, hexane, nonane and decane with varying volumes were used. Also, a developed thermodynamic model based on micellization and PC-SAFT (perturbed chain modification Statistical Associating Fluid Theory) has been proposed to account asphaltene precipitation during solvent titration conditions and the proposed model was verified using experimental data obtained in this work and also with those reported in the literature. In the proposed micellization model the PC-SAFT EOS (cubic equations of state) is applied to predict the onset point and to perform flash calculations. In order to compare the performance of the proposed micellization model, the asphaltene precipitation experimental data were correlated using existing micellization model and the proposed micellization model. The results show that the proposed micellization model predicts more accurately the asphaltene precipitation experimental data and is in agreement with the experimental data of reported in this work and with those reported in the literature with a mean square error of 0.9%. Therefore the predictions obtained from this proposed model, resulted in a good agreement with experimental data which shows a significant improvement in comparison to the previous micellization model in the available literature.     

汽油馏分烃化合物组分的蒸汽压计算

应用立方型状态方程计算气液相平衡,求取汽油馏分中烃化合物组分在任意指定温度下的饱和蒸气压,为汽油馏分的气液相平衡计算提供支持。方程求解通过编程实现,输入参数包括化合物的临界温度、临界压力和偏心因子,输出为对应温度下化合物的蒸气压。利用该方法可以计算汽油馏分中所包含的400多种烃化合物的蒸气压,比较在37.8 ℃下的蒸气压计算值和实测值,结果吻合较好,轻烃化合物蒸气压的平均相对误差低于1%,汽油全馏分范围内烃化合物蒸气压的平均相对误差低于4%。     

Development of an Artificial Neural Network Algorithm for the Prediction of Asphaltene Precipitation

Abstract

The precipitation and deposition of crude oil polar fractions such as asphaltenes in petroleum reservoirs considerably reduce rock permeability and oil recovery. Therefore, it is of great importance to determine how and how much the asphaltenes precipitate as a function of pressure, temperature, and liquid phase composition. The authors designed and applied an Artificial Neural Network (ANN) model to predict the amount of asphaltene precipitation at a given operating condition. Among this training, the back-propagation learning algorithm with different training methods was used. The most suitable algorithm with an appropriate number of neurons in the hidden layer, which provides the minimum error, was found to be the Levenberg-Marquardt (LM) algorithm. An extensive experimental data for the amount of asphaltene precipitation at various temperatures (293–343 K) was used to create the input and target data for generating the ANN model. The predicted results of asphaltene precipitation from the ANN model was also compared with the results of proposed scaling equations in the literature. The results revealed that scaling equations cannot predict the amount of asphaltene precipitation adequately. With an acceptable quantitative and qualitative agreement between experimental data and predicted amount of asphaltene precipitation for all ranges of dilution ratio, solvent molecular weight and temperature was obtained through using ANN model.     

Prediction of asphaltene precipitation from n-alkane diluted crude oil by using PC-SAFT equation of state

The precipitation tendency of heavy organics such as asphaltene has posed great challenges for petroleum industry, and thus study of asphaltene precipitation amount and formation conditions seems to be necessary. One of the most common approaches for prediction of asphaltene precipitation is using thermodynamic models. In this study a PC-SAFT equation of state (EOS) is used to predict asphaltene precipitation in two Iranian dead oil samples. Asphaltene content is obtained by filtration method of the oil samples diluted with specific concentrations of different normal alkanes. Also liquid-liquid equilibrium is used for characterization of oil sample into one heavy phase (asphaltene) and another light phase (saturates, aromatics, and resin). Calculations show that the developed model is highly sensitive to interaction parameter between oil fractions. Prediction results were improved due to using Chueh-Prausnitz equation. The results indicate good potential of PC-SAFT EOS in the prediction of asphaltene precipitation in crude oil samples diluted with different normal alkanes. The model error is <5% and the model precision is increased by reducing the number of normal alkane carbons.     

沥青质沉淀点的测定与模型化计算

通过自行设计、组装了一套采用透光率法测定原油体系沥青质沉淀点的实验装置,并采用该仪器测定了6个浓度的沥青质+甲苯体系及4个浓度的原油+甲苯体系的正戊烷和正己烷沥青质沉淀点。模型计算中采用目前具有代表性的Hirschberg沥青质沉淀溶解度模型以拟合实验数据。结果发现由实验数据回归得到的原油溶解度参数明显高于状态方程法的计算结果;原始条件下沥青质的溶解度参数值明显低于被提纯后沥青质的溶解度参数值。     

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.     

油气燃料临界燃烧和超临界燃烧的基础理论

利用热力学基本定律和常用状态方程vdW EOS、RK EOS、SRK EOS、PR EOS及PG EOS，对CH₄、C₂H₆、C₃H₈、C₈H₁₈、C₁₆H₃₄等的临界温度、临界压力、临界比容三者之间的相互关系进行了理论计算。还计算了He、H₂、N₂、O₂、CO、CO₂和水蒸气等的物性参数，并分别与其临界试验参数进行了对比分析。结果表明：理想气体状态方程PG EOS得到的结果最差，它对这些物质临界压力计算的平均误差高达260%以上，对临界温度计算的平均误差也在72%以上；而PR EOS得到的结果最好，且对临界温度计算的平均误差不足0.2%，最大误差在0.6%以下，对临界压力计算的平均误差则小于0.8%；其余EOS对物质临界参数的描述则介于二者之间。其中：vdW EOS对这些物质临界压力描述的最大误差在60%以上，平均误差在14%以上；RK EOS和SRK EOS对临界压力描述的最大误差均在13%以上，平均误差在2.6%左右，但SRK EOS对临界温度的总体描述要好于RK EOS。因此，PG EOS不能用来表述这些物质临界参数之间的相互关系，而PR EOS和SRK EOS可对这些物质临界参数之间的相互关系作出相对较好的描述。     

原油沥青沉降模拟方法

油田生产时，沥青从油藏原油中沉降出来会堵塞地层井眼和生产设备，严重影响正常生产，许多文献详尽地描述了沥青沉降的危害及处理方法，这些方法一定程度上能维持气井的生产，但往往耗时多且处理费用昂贵，在注气提高采收率过程中，沥青沉降也经常严重妨碍采收率的是高，因此，有必要研究沥沉降的理论预测方法，帮助人们在设计注气与生产方案时充分考虑这一影响因素，以避免或减小它的危害。     

气液固三相相平衡计算

根据对油气烃类体系相态特征的一般性认识与文章中提出的沥青组分特征化方法,导出了与之相应的有其自身特殊性的气液沥青三相相平衡物料平衡方程组.用考虑沥青沉降三相闪蒸数值算法,能对沥青沉降进行有效的量化模拟计算.此外,文章提出了大的交互作用系数可以描述沥青与原油中轻烃不相溶性的观点.结合实例计算,给出了沥青质参考逸度的计算、饱和压力和沥青沉降量的拟合方法.     

气液固三相相平衡热力学模型与计算方法

原油中含有大量的高分子有机固相物质，因此，要准确地描述油气体系相平衡，必须对气液固三相相平衡进行研究，在对高分子有机烃类沥青沉降机理有了一定的认识的基础上，提出了大的交互作用系数，可以描述沥青与原油中轻质不相容性的程度。根据对油气烃类混合物体系的一般性认识与提出的沥青组分特征化方法，导出了与之相应的有其自身特殊性的气液沥青三相相平衡物料平衡方程组，用考虑沥青沉降三相闪蒸数值算法，对沥青沉降进行有效的理化模拟计算，此外，结合实例分析，给出了沥青质参考逸度的计算，饱和压力和沉降量的拟合方法。     

Spectrophotometric Measurement of Asphaltene Concentration

Abstract

Typically, when ultraviolet and visible absorbance of asphaltenes is employed to measure asphaltene concentration, linear calibrations of absorbance vs. asphaltene concentration are prepared from a sample of asphaltenes in a given solvent. This calibration is shown to be sensitive to: (a) the inorganic solids content of the asphaltenes; (b) physical–chemical differences between asphaltenes from different sources or extracted with different methods; and (c) selective adsorption of asphaltenes on liquid–liquid or solid–liquid interfaces. Calibration constants were determined at wavelengths of 288 and 800 nm for samples of Athabasca and Cold Lake asphaltenes obtained using different extraction methods, from precipitation experiments, and from adsorption experiments on water-in-hydrocarbon emulsions and on powdered metals. It was found that the inorganic solids content did not affect absorbance but the asphaltene concentrations must be corrected to a solids-free basis for accurate results. Calibration constants were found to correlate to the average associated molar masses of the asphaltenes. Therefore, any change in molar mass of asphaltenes during the course of an experiment may change the calibration constant. Partial precipitation and the selective adsorption of asphaltenes can lead to a change in the molar mass of asphaltenes left in solution. The corresponding change in the calibration constants can lead to errors of 5–25% in the estimated concentration.     

The Prediction of Asphaltene Precipitation from Crude-Oils

Abstract

Asphaltene precipitation is undesirable deposition that causes difficult problems in oil production and transportation. A molecular thermodynamic model is proposed for predicting the asphaltene precipitation under live oil conditions and at a wide range of pressures and different solvent ratios. In this model, it is assumed that the precipitation phenomenon is a reversible process, and an equation of state is employed for phase behavior prediction. The vapor and liquid equilibrium calculations are performed separately and sequentially. The characterization of unknown-heavy fraction of petroleum (C₇₊) is obtained by the generalized molar mass distribution model, in which C₇₊ is represented by four pseudocomponents. The two heaviest pseudocomponents of C₇₊ are identified as asphaltenic components, are also considered as precipitating components. The model is verified by its ability to prediction of asphaltene precipitation in different thermodynamic conditions. It has been shown that the calculated results are in good agreement with the experimental data.     

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.     

High—Efficiency Mist Eliminators

Mist eliminators are designed to catch: liquid drops from the gas stream or vapor in treating and drying natural gas; coke, metal, and asphaltene particles in vacuum distillation of atmospheric resid; solvent drops in selective treatment of lube oils, and for separating liquid drops in separators installed in front of compressors, etc.     

Classification of Devices for Introduction and Distribution of Light Phase in Mass-Exchange Columns (Survey of Patents)

Chemical and Petroleum Engineering - A classification of devices for the supply and distribution of light phase (gas, vapor, mixture of gas and vapor, liquid) is proposed. A critical survey of the...     

PREDICTION OF THE PHASE BEHAVIOR OF ASPHALTENE MICELLE / AROMATIC HYDROCARBON SYSTEMS

Asphaltene particles can be dissolved in an organic molecular system, and they may form micelles in the presence of excess amounts of aromatic molecules. The existent thermodynamic theory of phase separations in micellar solutions, developed for water/ amphiphile solutions, it is generalized and applied to the prediction of the phase separation in micellar solutions of aromatic/ asphaltene at various temperatures and normal pressure, in this paper. Three forms for asphaltene micelle formation are proposed. The calculations performed are indicative of the existence of one new phase in the asphaltene micelle formation region. The results obtained are compared with the available critical micelle concentration data.     

Investigation of Molecular Weight and Aggregation of Asphaltenes in Organic Solvents Using Surface Tension Measurements

Abstract

The physico-chemical behavior of asphaltene is very useful to assist prediction and control the mechanisms of asphaltene deposition during the production of petroleum fluids. It has been realized that the first step in the formation of precipitated asphaltene particles is the self-aggregation mechanism to form colloidal particles or pseudo-micelles in several solvents. In this work, the critical micelle concentration (CMC) of two asphaltene fractions extracted from vacuum residues (VR) were obtained by surface tension measurements, using aromatic solvents. The molecular weight (1094-565 g/mol), calculated from average radii of the asphaltene molecules adsorbed at the air–solvent interface, are also in agreement with the values for small aggregates reported using small angle X-ray scattering.