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.     

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.     

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.     

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.     

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.     

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.     

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.     

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.     

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.     

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。     

The Effective Diffusion/Dispersion Coefficient in Vapor Extraction of Heavy Oil

Abstract

In this study, a new correlation for determination of effective diffusion/dispersion coefficients in the vapor extraction of heavy oil/bitumen (VAPEX) is introduced. This model takes into account the solvent concentration as well as the drainage height and permeability dependency of these coefficients. The concentration dependency in this model stems from the mixture viscosity changes, while the height dependency appears directly in the correlation. The correlation was obtained using the experimental results of the VAPEX experiments that were conducted with physical models of varying sizes and different permeability sand-packs. Estimation of a proper mass transfer coefficient has been a challenging issue for the analytical and numerical simulation of the VAPEX and other similar processes. Incorporating the effect of drainage height on dispersion with a concentration-dependent diffusivity model enables one to estimate the dispersion coefficient values involved in this process.     

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.     

A PSO-ANFIS framework for prediction of density of bitumen diluted with solvents

One of the important properties in petroleum engineering calculations in heavy oil reservoirs is the density of bitumen diluted with solvents. It is required in newly developed solvent based enhanced oil recovery methods. Hence, developing accurate models for prediction of this parameter is essential. To tackle this issue, this study presents an accurate model based on adaptive neuro-fuzzy inference system trained by particle swarm optimization (PSO-ANFIS) for estimation of density of bitumen diluted with solvents and hydrocarbon mixtures using experimental data from literature. The accuracy and reliability of results were evaluated by utilizing various statistical and graphical approaches and comparing the predictions of the developed model with literature models. The analysis showed that the PSO-ANFIS model is capable to predict the experimental data with acceptable error and high accuracy. The predictions of the PSO-ANFIS model were also better than the literature models.     

两种油砂加工方法的对比研究

分别采用溶剂萃取法和流化热转化法对内蒙古图牧吉油砂的加工方法进行了研究。溶剂萃取法可以得到油砂中几乎所有油品,但其液体产品具有高密度、高黏度及高残炭等特点,后续加工难度大;流化热转化法可以得到油砂中82.3%的油品,与溶剂萃取法相比,其液体产品的性质得到了较大程度的改善。对流化热转化得到的液体产品进行分馏和分析,其中汽油、柴油收率之和达到了37.32%,但是需要进一步精制才能达到国家油品标准的质量要求;重油收率达到了62.68%,可以通过进一步掺炼实现其轻质化。     

FURTHER INVESTIGATIONS ON THE USE OF BITUMEN AND HEAVY OILS TO PROCESS COAL

ABSTRACT

The use of oil sands bitumen, heavy oil and liquids derived therefrom can be successfully used to liquefy an Alberta subbituminous B coal. The data indicate that by co-processing coal with these solvents, coal conversions and yields of liquid products are favorably compared with those obtained using anthracene oil as solvent.     

Determination of gas dispersion in vapor extraction of heavy oil and bitumen

In this work, a mathematical model is developed and simulated to determine gas dispersion along with solubility during the vapor extraction (Vapex) of live oil from a laboratory scale physical model. The physical model is a rectangular block of homogenous porous medium saturated with heavy oil and bitumen. At a given temperature and pressure, the block is initially exposed on its side to a solvent gas, which diffuses into the medium and gets absorbed. The absorption of gas reduces the viscosity of heavy oil and bitumen causing it to drain under gravity. The low-viscosity “live oil” is produced at the bottom of the porous block. The production of live oil with time is accompanied by the shrinkage of oil in the block as well as its increased exposure to gas from top. These phenomena of Vapex are described by the mathematical model, which is used to calculate live oil production with various values of gas solubility and dispersion. Their optimal values are determined for the vapor extraction of Cold Lake bitumen with butane by matching calculated live oil production with its experimental values published earlier.     

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.     

轻质溶剂辅助蒸汽驱蒸汽腔扩展特征

在矿场即将实施轻质溶剂辅助水平井蒸汽驱开采薄层稠油油藏之际,选择用正己烷溶剂作为轻质溶剂,先采用二维物理模拟技术,研究了添加溶剂后蒸汽腔的展布规律和对生产动态的影响规律;之后,为进一步研究溶剂在蒸汽腔中的运移规律和对温度场、黏度场和含油饱和度场的影响规律,采用CMG公司的CMG-STARS模块,基于二维物理模型参数,对溶剂辅助蒸汽驱进行了数值模拟。研究表明:薄层稠油油藏在采用水平井蒸汽驱过程中添加单组分轻质溶剂能够有效降低蒸汽腔内部及蒸汽腔前缘的原油黏度,从而提高沿生产井方向的吸汽能力和驱油效率,与普通蒸汽驱对比,其具体表现为蒸汽腔体积大,沿注汽井方向扩展快,沿生产井方向扩展均匀,蒸汽前缘突破快,最终的波及范围大;生产过程中几乎无稳产阶段,且蒸汽前缘抵达生产井时产油速度达到峰值,之后高含水阶段发生汽窜且产油量小,最终驱替效率高。因此,添加溶剂辅助蒸汽驱相对于常规蒸汽驱可以有效降低地下稠油黏度,并且提高蒸汽在地层中的波及范围,从而高效地开发薄层稠油油藏。     

Geochemical evaluation of low salinity hot water injection to enhance heavy oil recovery from carbonate reservoirs

Although low salinity water injection (LSWI) has recovered residual oil after the conventional waterflood, highly viscous oil has remained in heavy oil reservoirs. Hot water injection is an economic and practical method to improve oil mobility for viscous oil reservoirs. It potentially controls temperature-dependent geochemical reactions underlying the LSWI mechanism and oil viscosity. Therefore, this study has modeled and evaluated a hybrid process of low salinity hot water injection (hot LSWI) to quantify synergistic effects in heavy oil reservoirs. In comparison to seawater injection (SWI) and LSWI, hot LSWI results in more cation ion-exchange (Ca²⁺ and Mg²⁺) and more wettability modification. Hot LSWI also reduces oil viscosity. In core-scaled systems, it increases oil recovery by 21% and 6% over SWI and LSWI. In a pilotscaled reservoir, it produces additional oil by 6% and 3% over SWI and LSWI. Probabilistic forecasting with uncertainty assessment further evaluates the feasibility of hot LSWI to consider uncertainty in the pilot-scaled reservoir and observes enhanced heavy oil production. This study confirms the viability of hot LSWI due to synergistic effects including enhanced wettability modification and oil viscosity reduction effects.     

Liquid-phase Mutual Diffusion Coefficients for Heavy Oil + Light Hydrocarbon Mixtures

Abstract:

Liquid-phase mutual diffusion coefficients are a key parameter in reservoir simulation models related to both primary production and envisioned secondary recovery processes for heavy oil and bitumen. The measurement of liquid-phase mutual diffusion coefficients in bitumen and heavy oil + light hydrocarbon or gas mixtures present numerous experimental and data analysis challenges due to the viscosity and opacity of the mixtures, the variability of density, viscosity and mutual diffusion coefficient with composition, and the multi-phase nature of these mixtures. Data analysis challenges are particularly acute. For example, recently reported mutual diffusion coefficient values for liquid mixtures of bitumen + carbon dioxide vary over three orders of magnitude when different analysis methods are applied to the same experimental data. In this contribution, we illustrate the importance of measuring composition profiles within liquids as a function of time, as a basis for mutual diffusion coefficient computation, and for allowing explicitly for the variation of diffusion coefficient and liquid density with composition in the analysis of composition profile data. Such inclusions eliminate apparent temporal variations of mutual diffusion coefficients and yield values consistent with relevant theories and exogenous data sets. Liquid-phase mutual diffusion coefficients computed for the mixtures Athabasca Bitumen + pentane and Cold Lake Bitumen + heptane exemplify the experimental and data analysis approaches.