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.      

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.      

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.     

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.     

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.     

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%.      

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.     

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.     

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 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.     

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.      

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 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.     

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).     

1-Hexyl-3-Methylimidazolium Chloride-Potassium Carbonate Aqueous Two Phase System: Equilibrium Characteristics and BSA Partitioning Behavior

The partitioning behavior of the model protein (bovine serum albumin) was investigated in ionic liquid (1-hexyl-3-methylimidazolium chloride) -salt (potassium carbonate) based aqueous two phase system (ATPS). The phase diagram with binodal curve and tie lines for the selected ATPS was developed at different temperatures and analyzed through effective excluded volume (EEV) and Othmer-Tobias and Bancroft equations, respectively. The influence of various process parameters like the ionic liquid and salt concentration, system temperature, tie line length, phase volume ratio, and neutral salt addition on partition coefficient/extraction efficiency of BSA protein was evaluated.