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.     

The Use of Microscopic Bitumen Froth Morphology for the Identification of Problem Oil Sand Ores

Abstract

Oil sand, which is found in various deposits around the world, consists mostly of sand, surrounded by up to 18 wt% bitumen. The largest deposits known are situated in northern Alberta, Canada, where reserves of bitumen are estimated to be 1.7 trillion barrels. Bitumen is similar to heavy oil, but with much higher viscosity and density. The two main commercial oil sand operations in Alberta are surface mines and use aqueous flotation of the bitumen to separate it from the rest of the oil sand. Under optimal conditions up to 95% of the bitumen can be recovered, but occasionally ores are mined that create problems in extraction, and recovery can drop to 70% or less. This article discusses the microscopic morphologies of various bitumen and heavy oil streams and their relationship to processing problems. The results of extensive microscopic work have demonstrated that the bitumen in an oil sand ore is the phase most susceptible to oxidation and that the resulting changes manifest themselves in particular microscopic structures. The presence and type of these structures can be related to the processing behavior of oil sand ores. Morphological features found in froths from commercial operations are similar to those found in froths from laboratory-prepared samples. The morphological features found in froths of oxidized ores have been categorized and quantified for a variety of samples and are referred to as degraded bitumen structures. Experiments in which fresh oil sand ores were subjected to low-temperature oxidation showed that bitumen froth morphology changed dramatically compared to that of nonoxidized ores for identical bulk compositions and extraction water chemistries.     

The Comparison of Bitumens from Oil Sands with Different Recovery Profiles

Abstract

It has been proposed that, regardless of origin, the recovery of bitumen from oil sands is related to its viscosity. Asphaltene and resin contents are known to affect the viscosity of bitumen. In this article we compare the composition of solvent-extracted bitumens from several Athabasca oil sands with very different recovery profiles. After careful removal of any associated mineral matter by ultra-centrifugation, each bitumen sample was separated into saturate, aromatic, resin, and asphaltene (SARA) fractions by an enhanced SARA technique. The individual components were then characterized by several complementary analytical techniques, including carbon, nitrogen, nitrogen, sulfur, size exclusion chromatography molecular weight (MW_n) plus proton and ¹³C nuclear magnetic resonance spectroscopy. Based on this comparison, we see no correlation between the recovery of bitumen and its composition.     

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.     

The Relationship Between Colloidal Chemistry and Ageing Properties of Bitumen

Abstract

The colloidal indices based on generic fractions, namely saturates (Sa), aromatics (Ar), resins (Re), and asphaltenes (As), were applied to evaluate the correlations between colloidal chemistry and ageing properties of bitumen. The results show that colloidal index Ip (value of Re/As) is positively correlated with penetration retention rate and ductility retention rate. The softening point increment is significantly influenced by colloidal index Is [value of As/(Sa + Ar + Re)]. The colloidal index Ic [value of (Ar + Re)/(Sa + As)] is obviously correlated with viscosity ageing index. Higher value of Ip and Ic, as well as lower value of Is, implies a better ageing resistance of bitumen.     

The Evaluation of Oil Sand Bitumen Produced From Inner Mongolia

On the basis of an analysis, the bitumen produced from Inner Mongolia oil sand belongs to a kind of sour naphthenic based oil with the properties of high density (ρ₂₀ = 0. 9996 g·cm^?3), high viscosity (υ₁₀₀ = 1553/(mm²·sec^?1)), rich resin, and asphalt. After a series of fractions is cut by true boiling distillation (TBP) SBD-β instrument and analyzed by corresponding instruments, the processing scheme of tar sand bitumen is proposed. The initial boiling point is 281°C, and the yield of diesel, lube oil, and residual oil is 4.54%, 16.73%, and 38.06%, respectively.     

Visbreaking of Chinese Oil Sand Bitumen

Abstract

Thermal visbreaking of inner Mongolia oil sand bitumen was conducted at several temperatures for different lengths of time in the laboratory. The viscosity of the thermally-treated oil was reduced dramatically with thermal treatment under the condition of adding 0.3 wt% anti-coking agent, the oil sand bitumen reacting at 410°C and 45 min. The kinematic viscosity (100°C) of visbreaking oil is reduced to 138.25 mm²·s^?1 and the qualities of it are conformed to 7# Chinese Standard for Fuel Oil, which can directly be regarded as product.     

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 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 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.     

Lube oil from blends of Nigerian Escravos light and Agbabu bitumen by empirical modeling

Abstract

Blends of Escravos light crude and Agbabu bitumen were prepared, distilled and characterized to investigate the effect of the blending on the yield and properties of the distillation fractions. The specific gravity, sulfur content, pour point and flash point of the blends were dependent on the proportions of the components. Regression analysis of the yields of distillation fraction gave robust empirical models. The predicted optimal blend of equal amounts of Escravos and Agbabu bitumen gave distillation fractions similar in properties and yield to the Russian Urals crude. Light components from the Escravos played enhanced solubilization of the heavier components in the bitumen.     

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。     

The Use of Microscopic Bitumen Froth Morphology for the Identification of Problem Oil Sand Ores

Oil sand, which is found in various deposits around the world, consists mostly of sand, surrounded by up to 18 wt% bitumen. The largest deposits known are situated in northern Alberta, Canada, where reserves of bitumen are estimated to be 1.7 trillion barrels. Bitumen is similar to heavy oil, but with much higher viscosity and density. The two main commercial oil sand operations in Alberta are surface mines and use aqueous flotation of the bitumen to separate it from the rest of the oil sand. Under optimal conditions up to 95% of the bitumen can be recovered, but occasionally ores are mined that create problems in extraction, and recovery can drop to 70% or less. This article discusses the microscopic morphologies of various bitumen and heavy oil streams and their relationship to processing problems. The results of extensive microscopic work have demonstrated that the bitumen in an oil sand ore is the phase most susceptible to oxidation and that the resulting changes manifest themselves in particular microscopic structures. The presence and type of these structures can be related to the processing behavior of oil sand ores. Morphological features found in froths from commercial operations are similar to those found in froths from laboratory-prepared samples. The morphological features found in froths of oxidized ores have been categorized and quantified for a variety of samples and are referred to as degraded bitumen structures. Experiments in which fresh oil sand ores were subjected to low-temperature oxidation showed that bitumen froth morphology changed dramatically compared to that of nonoxidized ores for identical bulk compositions and extraction water chemistries.     

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.     

Monitoring Bitumen Upgrading,Bitumen Recovery,and Characterization of Core Extracts by Hydrocarbon Group-type SARA Analysis

Abstract

Hydrocarbon group-type analyses are presented in this work for characterization of samples related to the Province of Alberta's abundant bitumen reserves that demand more effective ways of utilization and valorization. The main objective of the study is the evaluation of thin layer chromatography with flame ionization detection (TLC-FID) for rapid hydrocarbon group-type monitoring of bitumen samples and determination of the feasibility of each method for application with very small sample amounts. TLC-FID is a known technique for hydrocarbon (HC) group-type analysis. Different methods of utilization of TLC-FID are assessed here. The analytical techniques employed and data obtained are presented and compared. HC group types are presented in term of saturates, aromatics, resins, and asphaltenes (SARA). Applications are shown for three types of studies related to bitumen: with respect to ultradispersed catalytic bitumen upgrading; solvent-based enhanced bitumen production (Vapex); and characterization of organic extracts from reservoir cores. Techniques are evaluated and validated for each utilization and the preferred methodology indicated.     

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.     

Oxidative Degradation of Athabasca Bitumen,Fuel Oil,and Used Transformer Oil

Abstract

Oxidative cracking of bitumen, waxy fuel oil, and used transformer oils were carried out individually in an autoclave with 0.6 wt% of hydroquinone as a catalyst. The cracking process is conducted at 410°C in an atmosphere of oxygen gas of 0.15 MPa, for 30 min. The identification and quantitative determination of both the liquids and gases obtained during the cracking process are achieved using packed and capillary gas chromatography (GC) connected with suitable detectors. It was found that the degraded liquid products obtained have a higher percentage of lower hydrocarbons compared to the original feed stocks. Several analytical parameters including API gravity, calorific value, viscosity, density, pour point, etc., were used to evaluate the liquid product obtained. Also, the calorific values of the liberated gases were calculated and compared with that of natural gas. The cracked oil products were distilled and compared to their corresponding petroleum fractions. The cracked fractions have the same characteristics as their corresponding petroleum fractions with the exception of some properties that depend on the aromatic, naphthenic, and waxy nature of the virgin oil.     

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.     

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.