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.      

Asphaltene precipitation from latin american heavy crude oils. Effects of solvent aromaticity and agitation on particle size reduction

Asphaltenes from three crudes were precipitated with a mixture of n-heptane and toluene, the size of the particles formed under different solvent mixtures and different agitation regimes were studied. The kinetic size reduction of aggregates formed with an excess of precipitant agent is followed, contrary to other published studies where the kinetic followed is of growing particles. It was found that the particle size of precipitated asphaltenes decreases as precipitant aromaticity increases and agitation energy rises, which indicates the formation of aggregates bonded by weak forces, since the agitation applied was not of high energy, except for the ultrasonic device.     

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.      

Experimental Evaluation of the Additive Compounds Performance on Asphaltene Precipitation Using Automatic Titration Method

The use of soluble amphiphiles oil types provide the most practical and economical solution for crude oil treatment in order to control the asphaltene precipitation phenomenon. In this article, the inhibition performance of a number of new chemical additives to asphaltene precipitation is examined on two types of Iranian crude oil. An automatic titration method is used in experimental evaluation. The vegetable oil types (hazelnut, wheat germ, and walnut), organic acids (oleic and linoleic) and chemical additives (4-dodecylresorcinol and benzyl alcohol) displayed the highest capacity to inhibit asphaltene precipitation. A remarkable onset displacement is displayed by dodecyl resorcinol. The results also revealed that the vegetable oil have high potential in delaying asphaltene precipitation.     

Understanding the fluid phase behaviour of crude oil: Asphaltene precipitation

We present a simplified but consistent picture of asphaltene precipitation from crude oil from a thermodynamic perspective, illustrating its relationship to the familiar bubble curve via the calculation of constant-composition p-T phase diagrams that incorporate both the bubble curve and the asphaltene precipitation boundary. Using the statistical associating fluid theory (SAFT) we show that the position of the precipitation boundary can be explained using a very simple fluid model including relatively few components. Our results support the view that the precursor to asphaltene precipitation is a liquid-liquid phase separation due to a demixing instability in the fluid. Moreover, the bubble curve for these systems is seen to represent a boundary between regions of two-phase (liquid-liquid) and three-phase (vapour-liquid-liquid) equilibria.     

Detecting high-potential conditions of asphaltene precipitation in oil reservoir

Crude oil contains a wide range of components with different chemical natures. Complex molecules consisting of associated groups of polyaromatic sheets and alkyl side chains are known as asphaltene. Asphaltenes are insoluble in solvents such as n-heptane and n-pentane and soluble in benzene and toluene. Asphaltene causes serious damages around the wellbore and the reservoir by reducing permeability and plugging the pores. This paper includes a natural depletion test, performed on the bottom-hole sample and on a carbonate-core sample. The main emphasis is to identify high potentially damaged conditions in the reservoir from the asphaltene precipitation point of view. Stability of asphaltene was investigated by Saturates-Asphaltenes-Resins-Aromatics (SARA) analysis; moreover, asphaltene composition, permeability reduction, and porosity reduction were measured using the natural depletion in 4500, 3000, 2500, and 1450 psig via both static and dynamic approaches. At the pressures above the bubble point, asphaltene precipitation decreases as pressure increases, and the solubility model becomes dominant; on the other hand, below the saturation pressure, decrease in the pressure would decreases asphaltene precipitation and let the colloidal model dominate. It can be concluded that the maximum amount of asphaltene precipitation occurs near the saturation pressure. Asphaltene precipitation was then investigated through the core sample, using a novel scaling equation.     

Adsorption of asphaltene from crude oil by applying polythiophene coating on Fe3O4 nanoparticles

The objective of the present study was to investigate the potential use of applying polythiophene coating on magnetic Fe₃O₄ nanoparticles for the enhancement of asphaltene adsorption. Two stages of experimental were conducted. In the first stage, the ability of coated nanoparticles for asphaltene adsorption in synthetic asphaltene-toluene solution was evaluated. The effects of parameters such as nanoparticles concentration, initial concentration of asphaltene, and temperature were studied. In the second stage, the performance of the coated nanoparticles for the adsorption of asphaltene from crude oil was investigated under atmospheric pressure and a pressure-volume-temperature (PVT) apparatus was utilized for simulated reservoir conditions. Fe₃O₄ and Fe₃O₄-PT MNPs were synthesized using an effective co-precipitation method. The results of the first-stage tests indicated that the maximum adsorption capacity values for Fe₃O₄ and Fe₃O₄-PT MNPs were 0.79 and 1.09?mg?m^?2, respectively. The optimum value of nanoparticles concentration was approximately determined as 10?g?L^?1. According to the adsorption isotherms and kinetics, the Langmuir and pseudo-second-order Lagergren models were consistent with the experimental data, respectively. The average adsorption efficiencies for Fe₃O₄-PT and Fe₃O₄ MNPs were 78.98 and 65.94%, respectively. The results of the performed experiments on crude oil showed that Fe₃O₄-PT MNPs could adsorb asphaltenes from crude oil in a similar trend as synthetic asphaltene-toluene solution.