Applying FCM-ANFIS algorithm as a novel computational method for prediction of viscosity of bitumen and heavy alkane mixture

Recently the studies expressed that the noticeable number of oil reservoirs in all over the world are heavy oil and bitumen reservoirs. So the importance of enhancement of oil recovery (EOR) processes for heavy oil and bitumen reservoirs is highlighted. The Dilution of the reservoir fluid by solvents such as tetradecane is one of well-known methods for these types of reservoirs which effects oil recovery by decreasing viscosity. In the present study, Fuzzy c-means (FCM) algorithm was coupled with Adaptive neuro-fuzzy inference system (ANFIS) to predict viscosity of bitumen and tetradecane in terms of temperature, pressure and weight percent of tetradecane. The coefficients of determination for training and testing steps were calculated such as 0.9914 and 0.9613. The comparison of results and experimental data expressed that FCM-ANFIS algorithm has great potential for estimation of viscosity of bitumen and tetradecane.     

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.     

Modeling of the density of mixtures of Athabasca bitumen and a high boiling n-alkane

Recent studies revealed the more availability of heavy oil resources, such as bitumen than other types. So, the injection of solvents such as tetradecane with the aim of diluting bitumen is applied as an enhanced oil recovery (EOR) method for such reservoirs. This study has investigated the prediction of density for Athabasca bitumen–tetradecane mixture, under different temperature, pressure, and solvent's weight percent conditions, using a radial basis function neural network (RBF-NN) technique. Results were then compared with experimental values and values reported based on the previous correlation. MSE and R² values were 0.10496 and 1.00, respectively. Thus, this proposed model has been introduced as a very appropriate model for density prediction of bitumen–tetradecane mixture.     

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.     

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.     

Application of fuzzy c-means algorithm as a novel method to predict density of mixtures of Athabasca bitumen and heavy n-alkane

The significant number of oil reservoir are bitumen and heavy oil. One of the approaches to enhance oil recovery of these types of reservoir is dilution of reservoir oil by injection of a solvent such as tetradecane into the reservoirs to modify viscosity and density of reservoir fluids. In this investigation, an effective and robust estimating algorithm based on fuzzy c-means (FCM) algorithm was developed to predict density of mixtures of Athabasca bitumen and heavy n-alkane as function of temperature, pressure and weight percent of the solvent. The model outputs were compared to experimental data from literature in different conditions. The coefficients of determination for training and testing datasets are 0.9989 and 0.9988. The comparisons showed that the proposed model can be an applicable tool for predicting density of mixtures of bitumen and heavy n-alkane.     

Viscosity estimation of mixed oil using RBF-ANN approach

Diluting the bitumen and heavy oil with a liquid solvent such as tetradecane is one way to decrease the viscosity. The accurate estimation for the viscosity of the aforesaid mixture is serious due to the sensitivity of enhanced oil recovery method. The main aim of this study was to propose an impressive relation between the viscosity of heavy n-alkane and Athabasca bitumen mixtures based on pressure, temperature, and the weight percentage of n-tetradecane using radial basis function artificial neural network (RBF-ANN). Also, this model has been compared with previous equations and its major accuracy was evidenced to estimate the viscosity. The amounts of mean relative error (MRE %) and R-squared received 0.32 and 1.00, respectively. The endeavors confirmed amazing forecasting skill of RBF-ANN for the approximation of the viscosity as a function of temperature, pressure, and the weight percentage of n-tetradecane.     

储层中的沥青沉淀带及其对油气勘探的意义

在世界很多油气田的储集层中,都发现了天然形成的沥青沉淀带,说明储层中沥青沉淀带的存在是一个相当普遍的地质现象。前人的研究多集中在沥青沉淀的机理与成因判识、沥青的分布特征及其对储层物性的影响等方面,很少涉及沥青沉淀带对油气分布的影响,即使涉及的少数研究成果也都是从单一油田来进行分析,还未曾对其共性进行总结,此外,勘探家对钻探过程中出现的大量储层沥青重视不够,从而使得一些与沥青沉淀带有关的隐蔽型油气藏未能及时发现。根据前人的研究成果和实例,总结了3种不同成因储层沥青沉淀带的可能位置和储层沥青沉淀带的形成对油气分布的控制,指出沥青的沉淀对于油气藏不仅只有破坏作用,也有可能起到封堵作用而形成一种非常规类型的油气藏,关于这一点在勘探中应引起足够的重视。     

Data-driven modeling of heavy oil viscosity in the reservoir from geophysical well logs

Viscosity is the most crucial fluid property on recovery and productivity of hydrocarbon reservoirs, more particularly heavy oil reservoirs. In heavy and extra heavy oil reservoirs e.g. bitumen and tar sands more energy is required to be injected into the system in order to decrease the viscosity to make the flow easier. Therefore, attempt to develop a reliable and rapid method for accurate estimation of heavy oil viscosity is inevitable. In this study, a predictive model for estimating of heavy oil viscosity is proposed, utilizing geophysical well logs data including gamma ray, neutron porosity, density porosity, resistivity logs, spontaneous potential as well as P-wave velocity and S-wave velocity and their ratio (Vp/Vs). To this end, a supervised machine learning algorithm, namely least square support vector machine (LSSVM), has been employed for modeling, and a dataset was provided from well logs data in a Canadian heavy oil reservoir, the Athabasca North area. The results indicate that the predicted viscosity values are in agreement with the actual data with correlation coefficient (R²) of 0.84. Furthermore, the outlier detection analysis conducted shows that only one data point is out of the applicability of domain of the develop model.     

VISCOSITY AND SOLUBILITY OF MIXTURES OF BITUMEN AND SOLVENT

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.     

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.     

注有机极性气体开采稠油技术

针对储层薄、埋藏深的稠油油藏开发难度大及开采成本高的问题,在注溶剂萃取稠油技术（VAPEX）的基础上,提出向稠油油藏注入一种低饱和蒸汽压的有机极性气体PE提高采收率的采油技术。利用相态观察及黏度测定两种方法研究了该气体与稠油在气态和液态两种情况下的降黏效果。另外,还对比了该气体与VAPEX中常用烃类气体LPG对稠油降黏及萃取效果。实验结果表明:该气体在原油中具有很好的溶解性,能大幅度降低原油黏度;和LPG与稠油作用不同,该有机气体在液态状态下能大量分散稠油中的沥青,形成一种低密度低黏度混合相。现场应用表明注入该气体的措施井增产效果明显,因此,该注气技术可适用于一些埋藏深、油层薄、渗透性差和黏度高的稠油油藏。     

Estimation of bitumen emulsion dispersion using a light scattering method

Petroleum bitumen in the emulsified state is widely used as a binder in road and civil construction. Along with traditional quality parameters of bitumen emulsions the particle size is considered to be very important characteristic. The particle size of the dispersed phase affects the setting rate and viscosity of bitumen emulsions. The complete data on the particles size distribution can be obtained using such technical methods as light scattering and microscopy. In the study bitumen emulsions with three types of surfactants (cationic, nonionic, zwitterionic) have been prepared and their viscosity and decay index have been determined. The emulsions also have been studied with a microscope, as well as by the method of dynamic light scattering. The dynamics of particle size changes of the bitumen emulsions has been compared with the dynamics of the change in the basic parameters of the emulsions, such as viscosity and decay index, and the correlation between them has been found. Thus, using a modern laser and optical equipment, it is possible to estimate the quality of emulsions and, if necessary, to correct their composition.     

The effect of warm additive on the properties and behavior of an asphalt binder

Use of warm asphalt mix has been recently receiving a great attention because it allows decreasing production and distribution temperatures about 30–40°C by either reducing bitumen viscosity or enhancing mixture performance. These mixes also have other advantages such as a low pollution, reduced fuel consumption, and more implementation seasons. Despite these benefits, there is a constant concern about the performance of these mixes under the conditions such as permanent deformation, fatigue cracking, and low-temperature cracking. In the present study, two types of bitumen modifiers including liquid additive and Sasobit were used to adjust rheological properties of bitumen. After conducting the required tests on bitumen specimens prepared using these two additives and comparing the results obtained from two common and Strategic Highway Research Program tests under aged and nonaged conditions, it was revealed that, considering the viscosity of bitumen, these additives improve bitumen performance at ambient temperatures. Besides, the results indicate the improvement in rutting criteria (G^*/sinδ) and fatigue criterion (G^*×sinδ).     

不同粘度等级的再生剂对老化沥青再生性能的影响

研究了不同粘度等级的再生剂对老化沥青再生性能的影响。结果表明,不同粘度等级的再生剂对老化沥青的再生效果有明显差异。粘度低的再生剂对老化沥青软化点的降低、延度的提高、针入度的增大和粘度的减小更为明显。然而,对于轻度老化沥青,低粘度再生剂使其软化点和粘度降低幅度较大,对再生沥青的高温性能影响明显,因此轻度老化沥青宜选用粘度较高的再生剂,而重度老化沥青应选用低粘度的再生剂。     

Prediction of density and viscosity of bitumen

Abstract

It is well known fact that temperature and pressure significantly affects density and viscosity of bitumen. The present work utilizes Gene Expression Programming (GEP) approach to develop models to predict density and viscosity of bitumen. To evaluate the accuracy of proposed GEP based models, results reported by various researchers were utilized. This includes test results regarding Athabasca, Cold Lake and Gas free bitumen. The developed GEP based models were compared with the conventional empirical regression equations. The statistical analysis indicates that GEP based models work better than other existing models for density and viscosity of bitumen.     

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.     

川中地区寒武系龙王庙组沥青成因与油气成藏史

运用有机岩石学、有机地球化学和扫描电镜等方法和技术,深入研究了川中地区寒武系龙王庙组中高演化沥青的成因类型、形成期次和来源;并在此基础上,结合包裹体及构造演化特征等,恢复了安岳气田龙王庙组气藏的成藏史。结果表明:(1)川中地区龙王庙组储层中沥青均来自于下寒武统烃源岩,沥青成因类型多样,以热裂解成因为主;(2)沥青的形成分为3个阶段,第1阶段为氧化水洗型沥青的形成,第2阶段为沉淀型沥青的形成,第3阶段为热裂解成因沥青的形成;(3)安岳气田龙王庙组气藏先后经历了5期的油气充注,包括2期液态烃的充注、晚三叠世—早侏罗世干酪根裂解气的充注、晚侏罗世—早白垩世原油裂解气的充注以及喜马拉雅期圈闭改造调整阶段干气的充注。     

珠江口盆地白云凹陷储层沥青成因及其对油藏调整改造的启示

在珠江口盆地白云凹陷多个层系都发现了储层沥青。储层沥青丰度自上部的珠海组、珠江组至深部的恩平组、文昌组逐渐增加。储层沥青在物性较好的粗粒砂岩中多为零星状出现，而在细粒的粉砂岩和泥岩中主要呈脉状分布而且含量明显增加。几乎所有的储层沥青均没有显示出明显的各向异性，而且沥青反射率均小于2.0%，表明高温热裂解不是储层沥青形成的主要原因。沥青抽提物的分子地球化学组成特征显示，白云凹陷储层沥青抽提物中缺乏25-降霍烷系列，色谱基线没有明显的“UCM”鼓包、单井地层埋藏史显示储层也未遭受大幅度的抬升剥蚀，因此储层沥青也非生物降解成因。结合地质背景和油气藏特征分析认为，白云凹陷储层沥青的成因为：恩平组-文昌组烃源岩早期生成的原油聚集成藏以后，深部主要遭受了后期天然气的气洗作用。不同层位烃源岩、储层热演化的时间差异是导致白云凹陷油气藏发生调整，并转变为轻质油、挥发油等特殊油藏类型的重要原因。     

Effect of waste EVA (Ethylene Vinyl Acetate) and Waste CR(Crumb Rubber) on characteristics of Bitumen

In this research, waste Polymer Ethylene Vinyl Acetate (EVA) in three levels 3, 5 and 7 percent and waste crumb rubber in three levels 2, 4 and 6 percent were used as modifier of 85/100 refinery bitumen of Iran. In order to evaluate low temperature behavior of modified bitumen and it's compare with low temperature behavior of base bitumen, Superpave protocol has been used in this way that Bending Beam Rheometer test (BBR) has been done and with measuring creep stiffness indexes and creep rate, low temperature behavior of bitumen has been accounted according to SHRP standard. With doing Bending Beam Rheometer test (BBR), it is realized that these additive caused to improvement features of bitumen low temperature prior to base bitumen. In this way that 3% polymer Ethylene Vinyl Acetate (EVA) and 4% waste crumb rubber are chosen as the optimum mixture. Then with doing Elastic Recovery test, rate of Elastic Recovery of modified bitumen is recognized including different percent of additives and with doing Rotary Viscometer test, viscosity (Pa.s) has been measured for examining pumping ability of modified bitumen.