Experimental Investigation of the Effects of Different Parameters on the Rate of Asphaltene Deposition in Laminar Flow and Its Prediction Using Heat Transfer Approach

In this study, asphaltene deposition from crude oil on the pipe surface has been studied experimentally using a novel designed test loop. Washing technique is used to quantitatively measure the rate of asphaltene deposition during laminar flow in the steel pipe. The effects of oil velocity, asphaltene content, and surface temperature on the thickness of asphaltene deposition are investigated. The results show that the asphaltene deposition rate increases with increasing surface temperature, results in asphaltene content reduction of the flowing crude oil. As the oil velocity increases, less deposition was noticed on the surface of the pipe. Besides, thermal approach was applied to the experimental procedure which shows good agreements between the predicted thickness and the measured value from the test loop.     

Evaluation of Asphaltene Inhibitors Effect on Aggregation Coupled Sedimentation Process

The aggregation coupled sedimentation of asphaltene particles in toluene and heptanes mixture are investigated in the presence of two asphaltene inhibitors including, dodecyle resorcinol (DR) and hazelnut oil. An image processing technique is adopted in the three sections in a settling column setup. The aggregation and sedimentation phenomena are evaluated through a time-driven Monte Carlo (MC) model. The results are in good agreement with the obtained experimental results. The results indicate that in controlling asphaltene sedimentation DR is more effective. The results also revealed that the hazelnut oil has a capacity to inhibit asphaltene sedimentation at high concentration.     

Morphology and Size Distribution Characterization of Precipitated Asphaltene from Live Oil During Pressure Depletion

The size and morphology of asphaltene aggregates, precipitated from live oil by pressure depletion at the reservoir temperature was studied using scanning electron microscopy and atomic force microscopy. The experimental studies showed that the mean size of aggregates increased when pressure decreased. The results indicate that the morphology of aggregates was changed from amorphous spherical and elliptical shapes to irregular. A bimodal distribution function was able to describe the size distribution in pressure range of 500 to 3500 psi. At higher pressure, the unimodel was able to represent the size distribution. The results showed reduction in live oil stability and asphaltene aggregation with pressure drop.     

Developing Thermodynamic Micellization Approach to Model Asphaltene Precipitation Behavior

An little known yet significant issue in petroleum production processes in petroleum reservoirs is asphaltene precipitation/deposition. Asphaltene has not only a fuzzy and vague nature but it also can cause detrimental problems like reservoir blockage and, as a result, low oil recovery. To tackle this issue, many researchers have attempted to monitor asphaltene behavior versus thermodynamic conditions. A thermodynamic micellization approach is implemented in this work to describe asphaltene precipitation behavior for two sample fluids from Iranian reservoirs. First, the basic structures of the addressed approach and different contributions to Gibbs free energy of micellization proposed by Victorov and Firoozabadi (VF) are demonstrated. Second, a detailed sensitivity analysis with respect to the model parameters is performed by utilizing a new calculation strategy. Finally, a comparison between the predicted precipitation curve and the experimental one is illustrated; moreover, comparing our results with those reported by Victorov proves the superiority of the new strategy over the conventional one. The significance of this study shows the effect of each micellization parameter on the asphaltene precipitation behavior curve and illustrates the ability of the micellization approach evolved by VF in monitoring the effect of pressure on asphaltene precipitation using the new calculation procedure. Outcomes from this study could couple with commercial reservoir simulation software to improve precision and integrity for designing robust and effective production units.     

Prediction of Crude Oil Asphaltene Precipitation Using Support Vector Regression

Precipitation and deposition of asphaltene during different recovery processes is an important issue in oil industry which causes considerable increase in production cost as well as negatively impacting in production rate. In this study, support vector regression as a novel computer learning algorithm was utilized to estimate the amount asphaltene precipitation from experimental titration data. Also, the result of support vector regression modeling was compared with the artificial neural network model and the scaling equation. Results show acceptable agreement with experimental data and also more accurate prediction in comparison to artificial neural network and scaling equation.      

19F Dynamic Nuclear Polarization and SEM in Suspensions Consisting of Fluorobenzene Derivatives and Asphaltene Extracted from MC-800 Liquid Asphalt

A variety of analytical techniques, such as scanning electron microscopy and ¹⁹F dynamic nuclear polarization (DNP) methods, are applied to characterize asphaltene extracted from MC-800 liquid asphalt in fluorobenzene derivatives at 1.53 mT and at room temperature. Different solvents show variable affinities for the asphaltene surface. The low field EPR spectrum of the asphaltene/hexafluorobenzene sample was recorded. The DNP parameters were determined. Additionally, the interactions between the nuclei of the solvent and the electrons delocalized on the asphaltene are interpreted. Not only dipolar but also scalar interactions between the nuclear spin and the electron spin were found.     

Smart Determination of Difference Index for Asphaltene Stability Evaluation

Precipitation and deposition of asphaltene during different stages of petroleum production is recognized as problematic in oil industry because of the increase in production cost and the inhibition of a consistent flow of crude oil in different medium. Numerous correlations have been developed to determine asphaltene stability in crude oil. In this study, a novel ONN method was used to estimate difference index from SARA fraction data for rapid, accurate, and cost-effective determination of asphaltene stability. Neural networks are highly in danger of trapping in local minima. To eliminate this flaw, a hybrid genetic algorithm-pattern search technique was used instead of common back-propagation algorithm for training the employed neural network. A comparison between neural network and optimized neural network indicated superiority of optimized neural network.      

Prediction of Chemical Inhibitors Efficiency for Reducing Deposition Thickness Using Artificial Neural Network

Prediction of efficiency of chemical inhibitors to mitigation of deposition thickness is a key to developing crude oil transportation process. In this work, a feed-forward artificial neural network (ANN) algorithm has been applied to predict the influence of the mitigation effect of ethylene-co-vinyl acetate (EVA) copolymer and its combination with chloroform (C), acetone (A), P-xylene (PX), and petroleum ether (PE) on the deposition thickness in the pipeline. An optimized three-layer feed-forward ANN model using properties of the oil pipeline such as: inlet oil temperature, environmental (coolant mixture) temperature, oil Reynolds numbers; properties of injected inhibitor such as molecular weight, boiling point, and amount of injection; and time is presented. Different networks are considered and trained using 62661 data sets; the accuracy of the network is validated by 20888 testing data sets. To verify the network generalization, 29 different experiment data sets of four different set of inhibitors have been considered. It is found that the proposed ANN model is an alternative to experimentation and predicts deposition thickness without experimentation, vast information, and tedious and time-consuming calculations.     

Investigation of Oil–Asphaltene Slurry Rheological Behavior

The presence of asphaltene means additional difficulties related to transport and processing due to the increased crude oil viscosity caused by the asphaltene. For a better knowledge of the flow properties of asphaltene containing crude oils, it is necessary to understand how asphaltene affects the rheological properties. The aim of this article is to provide information on such rheological properties of oil–asphaltene slurry systems. The results of rheological experiments show that the non-Newtonian flow curves can be approximated by the Bingham plastic model to determine the apparent viscosity and the yield stress as a function of asphaltene concentration and temperature. An explanation is also provided for the observed behavior.     

Asphaltene Instability Trends of Light and Heavy Crude Oils

In this work, two Iranian crude oils diluted in 1-methylnaphthalene (1-MN) were titrated with selected n-alkanes. Subsequently, samples were observed microscopically to determine the onset of asphaltene precipitation. A series of micrographs from de-asphaltening were used to show visible changes of the asphaltene sizes, shapes, and frequencies by addition the n-heptane to the subsamples after 5, 6, 11, and 24 hour lag times. The refractive indices (RI) of the titrated mixtures at different temperatures below and above the onset conditions were measured aiming to establish the asphaltene instability trend. Results show that for the diluted light and heavy crude oils, the onset of asphaltene precipitation is rather a gradual process with an almost constant slope of RI decrease due to the separation of asphaltene clusters from the mixture. This is a kinetically controlled process. Furthermore, the nature of the precipitant is likely to play a notable role. The rate of RI decreasing with temperature was approximately 0.0004/°C for both tested crude oils.     

Investigation of Asphaltene Aggregate Size Distribution Under Aggregation and Breakage Phenomena Using Monte Carlo Simulation

In this article, the aggregation and breakage processes are simulated through Monte Carlo method for asphaltene aggregates under shear-induced petroleum mixtures. The simulation results are verified by the aggregate size distributions of two types of asphaltenes having different fractal dimensions extracted from Iranian crude oil types. The obtained aggregate size distributions are affected by shear rate, toluene to heptane ratios and the oil type. The dynamic evolution of asphaltene aggregates shows an ascendant trend with time until they reach a maximum average diameter and then descent to a steady-state size. The asphaltene fractal dimension affects the aggregation process.     

Application of CO2-Triggered Switchable Surfactants to Form Emulsion with Xinjiang Heavy Oil

Owing to the high acid number of Xinjiang heavy oil and incomplete demulsification after pipelining, this article discusses the application of CO₂-triggered switchable surfactants to the emulsified transport of several Xinjiang heavy oils in the pipeline. Results show that CO₂-triggered switchable surfactants promote the formation and stabilization of oil-in-water (O/W) emulsion in the absence of CO₂ as a base. The property parameters of heavy oils fundamentally influence the indigenous emulsifying agents. The emulsion is stable when the heavy oil has a high acid number and low asphaltene content, which is also affected by some physical factors.     

Fuzzy Assessment of Asphaltene Stability in Crude Oils

Knowledge about stability of asphaltene, determined by difference index, is of significant interest because of the many problems associated with asphaltene precipitation. This study followed two parallel fuzzy strategies for estimating refractive index (RI) of crude oil and refractive index of crude oil at onset of asphaltene precipitation (PRI) from Sara fraction data. Predicted RI and PRI were then utilized for easy and fast diagnosis of asphaltene stability by dint of calculating difference index (or ΔRI = RI – PRI). The experimental data reported in the literature have been used for model developing and checking. An acceptable agreement between fuzzy predicted values and experimental data confirmed the power of fuzzy logic technique in prediction of RI, PRI, and consequent ΔRI. In this study, ΔRI was not predicted directly mainly for two reasons. First, RI and PRI contain invaluable information themselves and predicting them fulfills the need for these information when they are desired. Second, dividing the problem into two simpler parts and solving them separately enhances the terminal accuracy of prediction. Although the regression accuracy for ΔRI was not completely satisfied, the classification accuracy for discriminating between stable and unstable situations was 100%.      

Experimental Study and Numerical Modeling of Rheological and Flow Behavior of Xanthan Gum Solutions Using Artificial Neural Network

This study investigated effect of temperature, concentration, and shear rate on rheological properties of xanthan gum aqueous solutions using a Couette viscometer at temperatures between 25°C and 55°C and concentrations of 0.25 wt% to 1.0 wt%. The Herschel–Bulkley model described very well the non-Newtonian behavior of xanthan gum solutions. Shear rate, temperature, and concentration affected apparent viscosity and an equation was proposed for the temperature and concentration effect valid for each shear rate. This article also presents an artificial neural network (ANN) model to predict apparent viscosity. Based on statistical analysis, the ANN method estimated viscosity with high accuracy and low error.     

Experimental Evaluation of the Inhibitors Performance on the Kinetics of Asphaltene Flocculation

The kinetics of asphaltene flocculation are studied on two types of Iranian crude oil. Kinetic studies are conducted by applying near infrared spectrophotometry. The presence of inhibitors in the sample reduces the amount of light absorption of crude oil sample. The effect of these inhibitors according to the initial changes in the light absorption of crude oil samples is studied. The obtained results indicate that the asphaltenes are the highest stabilized in presence of the vegetable oil types (hazelnut and walnut) and chemical compound (4-dodecylresorcinol).     

Asphaltene Precipitation Modeling Using Fuzzy Tuning of Scaling Equations

A major concern in the petroleum industry is asphaltene precipitation, which has negative impacts on production costs and recovery. The scaling equation is the most popular approach for modeling asphaltene precipitated out of solution in crude oils. Due to different values assigned for involved coefficients in scaling equations, they might overestimate or underestimate in some region relative to each other. This study proposes an improved strategy for tuning scaling equations and compensating effects of overestimation and underestimation through fuzzy rules. This strategy, called fuzzy tuning of scaling equations (FTSE), has a parallel framework, which gains outputs of different scaling equations and then introduces them to a fuzzy model as inputs. The fuzzy model breaks down the problem into subspaces through fuzzy membership functions and solves each region separately using fuzzy rules. Aggregating results of each subspace produces final model's output (i.e., FTSE output). Results indicated that FTSE performed more satisfyingly compared with individual scaling equations performing alone.      

Renovating Scaling Equation Through Hybrid Genetic Algorithm-Pattern Search Tool for Asphaltene Precipitation Modeling

The scaling equation is the most popular mathematical modeling of asphaltene precipitation as a problematic issue in petroleum industry. There are eight adjustable coefficients in the scaling equation that govern the quality of the fit between titration data and the scaling equation model. In this study, a hybrid genetic algorithm-pattern search (GA-PS) tool was employed to extract optimal values of the involved coefficients in the scaling equation through the stochastic search. For better performance of the GA-PS tool, dimensionality of the problem was broken into two simpler parts using the divide-and-conquer principle by introducing two fitness functions. The renovated scaling equation was compared with previous works; it was shown that the proposed method outperforms previous works.      

Prediction of Density,Surface Tension,and Viscosity of Quaternary Ammonium-Based Ionic Liquids ([N222(n)]Tf2N) by Means of Artificial Intelligence Techniques

In this work, thermophysical properties of quaternary ammonium-based ionic liquids (ILs) including density, surface tension, and viscosity are produced by two powerful artificial intelligence techniques: genetic function approximation (GFA) and artificial neural network (ANN). In proposed GFA and ANN models, the critical temperature and water content of studied ILs ([N_222(n)]Tf₂N with n = 5, 6, 8, 10, and 12) as well as operation temperature were given as the input parameters and the density, surface tension, and viscosity were predicted as the output results. The obtained results reveal that the selected input parameters are appropriate for prediction of thermophysical properties of quaternary ammonium-based ILs. In addition, the high statistical quality represented by various criteria and the low prediction errors of the presented models indicate that they can accurately predict the density, surface tension, and viscosity of new ILs without recourse to experimental data.     

Influence of Interfacial Rheological Properties on Stability of Asphaltene-Stabilized Emulsions

A series of oscillating droplet measurements have been performed on asphaltenes at the oil/water interface, in order to correlate the interfacial rheological behavior to their ability to stabilize emulsions. In the concentration sweep, the elastic modulus goes through a maximum around an asphaltene concentration of 0.05–0.10 g/l. This behavior was not in good correspondence with emulsion stability, which increased consistently from low to high concentrations. The decrease above 0.10 g/l was most likely an effect of diffusion of asphaltenes in the bulk to the interface, which became more significant at higher bulk concentrations. The rheology data as a function of concentration has been fitted to Butler's surface equation of state and the Lucassen–van den Tempel model. A decent correlation was found between emulsion stability and elasticity for both the effect of solvent aromaticity and pH. The elastic modulus displayed a gradual increase when xylene was mixed with heptane as the solvent, as was seen with emulsion stability. This was not caused by a significant increase of the adsorbed amount of asphaltene at the interface, as shown by a quartz crystal microbalance (QCM), but a more efficient reorganization of the already adsorbed asphaltenes. The ability asphaltenes displayed in stabilizing emulsions was significantly increased at both low and high pH, according to a previous study. The elastic modulus, on the other hand, only showed a very weak increase at pH 2, but a better correlation with emulsion stability above pH 8. From this it would appear that the dissociation of acid groups in the asphaltene structure at high pH has a bigger impact on the interfacial activity than the protonation of bases at low pH, while their effect on emulsion stability was the same.