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.      

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.     

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.     

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.     

Generation of Asphaltene Deposition Envelope Using Artificial Neural Network

The usefulness of the asphaltene deposition envelope (ADE) is that no asphaltene flocculation occurs at conditions outside the envelope. Since artificial neural network (ANN) is best at identifying patterns or trends in data, it is well suited for prediction or forecasting needs. ANN is also capable of addressing case specific problems that may be encountered in the field such as deposition of asphaltene. In this article, the high pressure, high temperature setup used to perform pressure depletion experiments at three different temperatures on one the Iranian live oils and the ADE generated using ANN.     

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).     

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%.      

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.     

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.     

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.     

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.     

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.     

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.      

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.     

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.     

Hybrid of Two Heuristic Optimizations with LSSVM to Predict Refractive Index as Asphaltene Stability Identifier

Asphaltene precipitation is a big challenge in the petroleum industry. This motivated us to develop a reliable model between refractive index and SARA fraction as a tool for the diagnosis of asphaltene stability. Least-square support vector machine (LSSVM), due to its several unique advantages, has been successfully verified as a predicting method in recent years. However, the success of LSSVMs depends on the adequate choice of the kernel and regularization parameters. We proposed the combination of two search algorithms to deal with the problem of support vector machine parameter selection. On this basis, we combined coupled simulated annealing (CSA) and the Nelder and Mead Simplex method to optimize the parameters. In this hybrid optimization, first, CSA finds suitable starting values and these are passed to the simplex method in order to tune the result. The LSSVM results are promising and accurate, and outperform both neural network and empirical models existing in literature.      

Electrocoagulation of Crude Oil From Oil-In-Water Emulsions Using a Rectangular Cell with a Horizontal Aluminium Wire Gauze Anode

Separation of crude oil from oil-in-water emulsions in a square batch electrocoagulation cell, using an aluminum screen as the sacrificial anode was studied, the cathode was a rectangular aluminum plate placed on the cell bottom below the anode. The oil separation efficiency was insensitive to the sodium chloride electrolyte concentration. Increasing current density increased the rate of oil separation from the emulsion. Also it was found that increasing the number of screens per stack at the same current intensity, does not affect the efficiency of oil separation. The removal doesn't depend on the initial pH in the range of pH ～3?8.     

Experimental Investigation on Yield Stress of Water-in-Heavy Crude Oil Emulsions in Order to Improve Pipeline Flow

An experimental study on yield stress of water-in-heavy crude oil emulsions has been carried out by using a HAAKE RS6000 Rheometer with a vane-type rotor. Several factors such as oil volume fraction, shear rate, temperature, and emulsifying agent on the yield stress of emulsions were investigated. Zero shear viscosity of heavy crude oil was 6000 mPas at 30°C, with a density 955 kg/m³. This study shows that the yield stress increases linearly with the increasing shear rate, and displays an exponential decay with increasing the temperature and oil volume fraction. Although the addition of emulsifying agent enhanced the stability of the emulsion, to some extent it also increased the yield stress, especially for the emulsions with high oil volume fractions. Therefore, to reduce the start-up force for the pipeline transport of water-in-heavy crude oil emulsions, the starting rate should be decreased, temperature increased, or oil volume fraction increased. These results are helpful to improve the transportation of water-in-heavy crude oil in pipeline.      

Experimental Design Approach to Investigate the Effects of Operating Factors on the Surface Tension,Viscosity, and Stability of Heavy Crude Oil-in-Water Emulsions

In this article, the effects of various operating factors on the surface tension, viscosity, and stability of two heavy oil types in water emulsions for pipeline transportation are studied using the Taguchi experimental design approach. The surface tension of heavy crude oil-in-water emulsion is decreased by increasing the emulsifier concentration while the stability of emulsions is increased. The viscosity and stability are increased by an increase in oil content. An increase in the salinity and mixing speed leads to an increase in the stability of emulsion.     

Experimental Investigation of Asphaltenes Flocculation Threshold in Crude Oils Using Different Methods: Comparative Study

Despite some recent progress trustworthy methods to predict the flocculation onset in crude oils are still needed. In this article, different experimental methods, like the density measurement method, the optical and the spot techniques based on the mixture of crude oil + toluene or cyclohexane + n-heptane, respectively, are applied to assess changes of aggregation occurring in crude oil under influence of physical and chemical factors as well as under variable temperature conditions. The solubility parameters of Hildebrand have also been used to predict the flocculation threshold. It turned out that the findings achieved by the spot method and the optical technique are in excellent agreement with break points of the reduced density curves obtained by the density measurement method for crude oils of different geographical origins. Using the solubility parameters of Hildebrand leads to comparable results. All these achievements confirm the density measurement method as a new possibility for reliably determining the flocculation onset in crude oils systems. With respect to temperature effects, changes of the oil microstructure could be detected as function of the temperature by means of the differential scanning calorimetry. In particular the thermal curves behavior of certain crude oils confirmed the existence of aggregation processes.