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.     

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.     

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.     

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.     

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.     

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.     

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).     

The effect of different solvents on the density of undersaturated athabasca bitumen: Application in VAPEX and ES-SAGD

Predicting the density of bitumen after solvent injection is highly required in solvent-based recovery techniques like expanding solvent-steam assisted gravity drainage (ES-SAGD) and vapor extraction (VAPEX) in order to estimate the cumulative oil recovery by these processes. Using experimental procedures for this purpose is so expensive and time-consuming; therefore, it is crucial to propose a rapid and accurate model for predicting the effect of various solvents on the dilution of bitumen. In this study, an adaptive neuro-fuzzy interference system is introduced to estimate the effect of methane, ethane, propane, butane, carbon dioxide, and n-hexane on the density of undersaturated Athabasca bitumen in wide ranges of operating conditions. The obtained results were in an excellent agreement with experimental data with coefficients of determination (R²) of 0.99997 and 0.99948 for training and testing datasets, respectively. Statistical analyses illustrate the superiority of the proposed model in predicting the bitumen density at different conditions.     

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.     

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.     

GEOCHEMICAL CHARACTERISTICS OF THE SKRUGARD OIL DISCOVERY,BARENTS SEA,ARCTIC NORWAY: A “PALAEO‐BIODEGRADED – GAS REACTIVATED” HYDROCARBON ACCUMULATION

This paper investigates the filling history of the Skrugard and Havis structures of the Johan Castberg field in the Polheim Sub‐Platform and Bjørnøyrenna Fault Complex, Barents Sea (Arctic Norway). Oil and gas occurs in the Early Jurassic and Middle Jurassic Nordmela and Stø Formations at Johan Castberg, and both free oil and bitumen are interpreted to be sourced from the Upper Jurassic Hekkingen Formation (Kimmeridge Formation equivalent). The geochemical characteristics of the petroleum from Skrugard and Havis, including the GOR, API and facies and maturity signatures, can be understood within a complex fill history which includes a palaeo oil charge, Tertiary uplift (>2 km), dismigration, in‐reservoir biodegradation, and late‐stage refill with gas. The API and GOR of the Skrugard oil are 31° and 60m³/m³, respectively. The petroleum is geochemically similar to that in the nearby Havis structure, to that in the Snøhvit region to the south of the Loppa High, and also to the petroleum recorded as traces in well 7219/9‐1, approximately 16 km SW of Johan Castberg field. However, the petroleum differs from the oil in the Alta well 7120/2‐1, located in the southern part of the Loppa High, illustrating the complexity of the regional petroleum systems. The Skrugard oil is of medium maturity (ca. 0.8–0.9% R_c), and is significantly biodegraded despite being gas‐saturated. Evidence for biodegradation includes the reduced concentrations of C₁₀‐C₂₅ n‐alkanes and the presence of a prominent unresolved complex mixture (UCM) in gas chromatogram traces. However non‐biodegraded C₄‐C₈ range hydrocarbons are also present in the reservoir. This suggests a recent charge of gas/condensate into the structure which therefore contains a mixture of palaeo‐degraded and unaltered petroleum. Oil‐type inclusions within authigenic quartz and feldspar from reservoir sandstones at Skrugard were analysed. The results indicate that the structure (present‐day depth 1276–1395m) underwent Tertiary uplift by ca. 2–3km following an earlier phase of oil emplacement. The presence of the oil type inclusions, both in the current gas zone (Stø Formation) and in the oil zone (Stø and Nordmela Formations), indicates that the positions of the oil‐water and gas‐oil contacts have changed over time. This is consistent with a recent gas charge to the upper part of the reservoir, and also with the gas being at dew point. These observations are supported by analyses of core extracts which show an increasing bitumen content towards the OWC, and the oil‐type bitumen in the present‐day gas zone. A charge history model for the Skrugard structure is proposed which integrates both the observations concerning the petroleum inclusions and the biodegraded oil together with observations of seismically‐monitored gas fluxes along the rim of the Loppa High. Improved understanding of the Skrugard structure and its filling history will assist exploration in similar settings in other parts of the Barents Sea and worldwide, particularly where multiple source rocks and a multi‐stage charge history have controlled reservoir filling.     

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.     

Characterization and Potential Utilization of the Asphalt Ridge Tar Sand Bitumen I. Gas Chromatography - Mass Spectrometry and Pyrolysis-Mass Spectrometry Analyses

ABSTRACT

The native Asphalt Ridge bitumen was separated into several boiling range fractions for detailed analysis and characterization. The lighter fraction (477–617 K) was evaluated for use as an aviation turbine fuel and the residue (>617 K) was evaluated for use as an asphalt. The 477–617 K fraction appeared to meet most of the specifications for high density aviation turbine fuels. The 617 K plus residue from the Asphalt Ridge bitumen can be classified as a viscosity grade AC-30 asphalt. Several physical properties were also measured to evaluate the potential of the 477–617 K fraction as high density-energy aviation turbine fuel after mild hydrotreating. The detailed structure of the low molecular weight fractions of the Asphalt Ridge bitumen (477–617 K and 617–711 K) was determined by combined gas chromatography and mass spectrometry. Additional insight regarding the chemical structure of the bitumen was also obtained by Fourier transform infrared analysis. The tentative identification of saturated and aromatic components in the 477–711 K fractions indicated that these can be related to biologically-derived compounds which are found in coal, petroleum, oil shale, and tar sand.     

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.     

Applying Grid partitioning based Fuzzy inference system method to estimate interfacial tension of brine and hydrocarbon

The oil recovery reservoirs and oil trapping in the reservoirs are extensively function of interfacial tension between brine and hydrocarbon, so estimation of interfacial tension becomes one of the interesting topics in petroleum industry. In this study, Grid partitioning based Fuzzy inference system method is utilized to forecast interfacial tension of hydrocarbon and brine based on various effective parameters such as ionic strength of brine, carbon number of hydrocarbon, pressure, and temperature. The estimated values of interfacial tension were compared with real interfacial tension of brine and hydrocarbon using graphical and statistical analyses. The determined coefficients of determination (R²) for training and testing phases were 0.9916 and 0.9447, respectively. The comparing analyses express that the Grid partitioning based Fuzzy inference system method has great ability in prediction of interfacial tension, and it can be used as an applicable tool in petroleum industry.     

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.     

VISCOSITY PREDICTION OF CARBON DIOXIDE + HYDROCARBON MIXTURES USING THE FRICTION THEORY

ABSTRACT

The general one-parameter f-theory model has been used in conjunction with the SRK and the PR EOS to predict the viscosity of well-defined carbon dioxide + hydrocarbon mixtures. The predicted viscosities are within the uncertainty appropriate for most industrial applications. Although the studied mixtures are simple representations of real oil mixtures with carbon dioxide, the f-theory approach can easily be extended to more complex scenarios, such as the simulation of carbon dioxide enhance oil recovery. Additionally, a comparison with the LBC model, which is a widely used model in the oil industry, has been carried out. In contrast to the f-theory models, the strong dependency that the LBC model has on the accuracy of the density is clearly evident for the kind of mixtures studied in this work. Furthermore, it is shown how the phase behavior complexity that carbon dioxide + hydrocarbon mixtures develop may have a direct influence on the performance of the viscosity modeling and prediction.