Test Methods for Determining Asphaltene Stability in Crude Oils

Abstract

The high cost of remediating asphaltene deposition in crude oil production and processing has necessitated the development of test methods for determining the stability of asphaltenes in crude oils. In the current work, the stability of asphaltenes in crude oils of varying API gravity is predicted using the Oliensis Spot Test, the Colloidal Instability Index, the Asphaltene–Resin ratio, and a solvent titration method with NIR solids detection. The test methods are described in detail and experimental data from them presented. The experimental stability data were validated via correlation with field deposition data. The effectiveness of the various tests as predictors of the stability of asphaltenes in oils is discussed. The Colloidal Instability Index and the solvent titration method were found to predict a crude oil's propensity towards asphaltene precipitation better than both the Asphaltene–Resin ratio and the Oliensis Spot Test. For oils with low asphaltene content where most stability tests fail, live oil depressurization is proposed as the test for predicting the stability of asphaltenes.     

Development of an Artificial Neural Network Algorithm for the Prediction of Asphaltene Precipitation

Abstract

The precipitation and deposition of crude oil polar fractions such as asphaltenes in petroleum reservoirs considerably reduce rock permeability and oil recovery. Therefore, it is of great importance to determine how and how much the asphaltenes precipitate as a function of pressure, temperature, and liquid phase composition. The authors designed and applied an Artificial Neural Network (ANN) model to predict the amount of asphaltene precipitation at a given operating condition. Among this training, the back-propagation learning algorithm with different training methods was used. The most suitable algorithm with an appropriate number of neurons in the hidden layer, which provides the minimum error, was found to be the Levenberg-Marquardt (LM) algorithm. An extensive experimental data for the amount of asphaltene precipitation at various temperatures (293–343 K) was used to create the input and target data for generating the ANN model. The predicted results of asphaltene precipitation from the ANN model was also compared with the results of proposed scaling equations in the literature. The results revealed that scaling equations cannot predict the amount of asphaltene precipitation adequately. With an acceptable quantitative and qualitative agreement between experimental data and predicted amount of asphaltene precipitation for all ranges of dilution ratio, solvent molecular weight and temperature was obtained through using ANN model.     

Prediction of Asphaltene Precipitation during Pressure Depletion and CO2 Injection for Heavy Crude

Abstract

In this work, a thermodynamic approach is used for modeling the phase behavior of asphaltene precipitation. The precipitated asphaltene phase is represented by an improved solid model, and the oil and gas phases are modeled with an equation of state. The Peng-Robinson equation of state (PR-EOS) was used to perform flash calculations. Then, the onset point and the amount of precipitated asphaltene were predicted. A computer code based on the solid model was developed and used for predicting asphaltene precipitation data reported in the literature as well as the experimental data obtained from high-pressure, high-temperature asphaltene precipitation experiments performed on Sarvak reservoir crude, one of Iranian heavy oil reserves, under pressure depletion and CO₂ injection conditions. The model parameters, obtained from sensitivity analysis, were applied in the thermodynamic model. It has been found that the solid model results describe the experimental data reasonably well under pressure depletion conditions. Also, a significant improvement has been observed in predicting the asphaltene precipitation data under gas injection conditions. In particular, for the maximum value of asphaltene precipitation and for the trend of the curve after the peak point, good agreement was observed, which could not be found in the available literature.     

An Experimental Calculation of Asphaltene Critical Properties in Crude Oil Reservoir Systems

High-molecular-weight hydrocarbons and asphaltenes are important constituents of petroleum and can cause problems related to crystallization and deposition of asphaltene. This article introduces an extension of the cubic equation of state (Peng-Robinson EOS) to describe asphaltene–toluene mixture vapor–liquid equilibria (VLE) and characterize pure asphaltene, and the critical properties of asphaltene were adjusted. Experimental data were used to examine the proposed procedure in an Iranian crude oil reservoir.

We determined the critical properties of asphaltene for this EOS using a two-phase vapor–liquid equilibrium calculation. The test results showed that the proposed procedure is adequate for predicting asphaltene precipitation data.     

Study of Asphaltene Precipitation Using Refractive Index Measurement

Abstract

The measurements of the refractive index of crude oils were utilized in this work to enhance the understanding of the behavior of asphaltenes in crude oil, specifically, their tendency to precipitate from crude oil. The onset of asphaltene precipitation was measured in eight crude oil samples, which were titrated with either heptane or pentane in order to induce precipitation of the asphaltenes. The refractive index of each sample was measured to find its relationship to asphaltene precipitation. The assumption that refractive index of a mixture is a linear combination of the refractive indexes of the individual components was verified. It was also found that mixtures of heptane or pentane and crude oil also followed this same behavior. However, as asphaltenes began to precipitate from the solution, the refractive index no longer followed this linear mixing rule. Careful analysis of the refractive index data for each of the crude oil samples revealed many interesting relationships between the refractive index data and the content of the different polar asphaltene fractions present. The refractive index of asphaltenes was predicted from the refractive index data of crude oils. The results suggest the possibility predicting the properties and characteristics of the asphaltenes contained in a crude oil simply by measuring the refractive index.     

A Thermodynamic Model for the Prediction of Asphaltene Precipitation

Abstract

Asphaltene precipitation in reservoirs, wells, and facilities can have a severe and detrimental impact on the oil production. Due to the extreme chemical complexity of the asphaltene and crude oil and the lack of comprehensive experimental data, the modeling of asphaltene precipitation in crude oil remains as a challenging task. In this article, a compositional thermodynamic model was developed to predict asphaltene precipitation conditions. The proposed model is based on a cubic equation of state with an additional term to describe the association of asphaltene molecules. Extensive testing against the literature data, including asphaltene precipitation from crude oil and solvent injection systems, concludes that the proposed model provides reasonable predictive results.     

Intelligent Modeling of Asphaltene Precipitation in Live and Tank Crude Oil Systems

Abstract

The study of asphaltene precipitation properties has been motivated by their propensity to aggregate, flocculate, precipitate, and adsorb onto interfaces. The tendency of asphaltenes to precipitation has posed great challenges for the petroleum industry. The most important parameters in asphaltene precipitation modeling and prediction are the asphaltene and oil solvent solubility parameters, which are very sensitive to reservoir and operational conditions. The driving force of asphaltene flocculation is the difference between asphaltene and the oil solvent solubility parameter. Since the nature of asphaltene solubility is yet unknown and several unmodeled dynamics are hidden in the original systems, the existing prediction models may fail in prediction the asphaltene precipitation in crude oil systems. One of ways in modeling such systems is using intelligent techniques that need some information about the systems; so, based on some intelligent learning methods it can provide a suitable model. The authors introduce a new implementation of the artificial intelligent computing technology in petroleum engineering. They have proposed a new approach to prediction of the asphaltene precipitation in crude oil systems using fuzzy logic, neural networks, and genetic algorithms. Results of this research indicate that the proposed prediction model with recognizing the possible patterns between input and output variables can successfully predict and model asphaltene precipitation in tank and live crude oils with a good accuracy.     

Determining the Particle Size of Asphaltenes

Abstract

Study of heavy organics and their behavior is of great importance while occurrence of their deposition in production and processing of hydrocarbon fluids. The physical properties of heavy organics, especially asphaltenes, have been a subject of controversy for several years. The aim of the present work is to determine and measure particle size of asphaltenes. Several mixtures of crude oil and n-heptane were prepared with various dilution ratios (R_v ). Two different techniques were employed to determine and measure the particle size of asphaltenes. The first was utilizing an OLYMPUS BX60, a polarizing microscope with an appropriate magnification, and the second was using high-resolution scanning electron microscopy (SEM) and X-ray diffraction techniques. The results revealed that the size of asphaltene particles is in the range of 1–4 μm for each mixture, regardless of its n-heptane content. Although the quantity of deposited asphaltene was increased by increasing the concentration of n-heptane in mixture, the particle size of asphaltene was independent of the n-heptane concentration.     

The Prediction of Asphaltene Precipitation from Crude-Oils

Abstract

Asphaltene precipitation is undesirable deposition that causes difficult problems in oil production and transportation. A molecular thermodynamic model is proposed for predicting the asphaltene precipitation under live oil conditions and at a wide range of pressures and different solvent ratios. In this model, it is assumed that the precipitation phenomenon is a reversible process, and an equation of state is employed for phase behavior prediction. The vapor and liquid equilibrium calculations are performed separately and sequentially. The characterization of unknown-heavy fraction of petroleum (C₇₊) is obtained by the generalized molar mass distribution model, in which C₇₊ is represented by four pseudocomponents. The two heaviest pseudocomponents of C₇₊ are identified as asphaltenic components, are also considered as precipitating components. The model is verified by its ability to prediction of asphaltene precipitation in different thermodynamic conditions. It has been shown that the calculated results are in good agreement with the experimental data.     

Modeling Asphaltene Precipitation Under Gas Injection and Pressure Depletion Conditions

Asphaltene precipitation is a major problem during primary oil production and enhanced oil recovery in the petroleum industry. In this work, a series of experiments was carried to determine the asphaltene precipitation of bottom hole live oil during gas injection and pressure depletion condition with Iranian bottom hole live oil sample, which is close to reservoir conditions using high pressure-high temperature equilibrium cell. In the majority of previous works, the mixture of recombined oil (mixture dead oil and associated gas) was used which is far from reservoir conditions. The used pressure ranges in this work covers wide ranges from 3 to 35 MPa for natural depletion processes and 24–45 MPa for gas injection processes. Also, a new approach based on the artificial neural network (ANN) method has been developed to account the asphaltene precipitation under pressure depletion/gas injection conditions and the proposed model was verified using experimental data reported in the literature and in this work. A three-layer feed-forward ANN by using the Levenberg-Marquardt back-propagation optimization algorithm for network training has been used in proposed artificial neural network model. The maximum mean square error of 0.001191 has been found. In order to compare the performance of the proposed model based on artificial neural network method, the asphaltene precipitation experimental data under pressure depletion/gas injection conditions were correlated using Solid and Flory-Huggins models. The results show that the proposed model based on artificial neural network method predicts more accurately the asphaltene precipitation experimental data in comparison to other models with deviation of less than 5%. Also, the number of parameters required for the ANN model is less than the studied thermodynamic models. It should be noted that the Flory and solid models can correlate accurately the asphaltene precipitation during methane injection in comparison with CO₂ injection.     

AN EXPERIMENTAL STUDY OF THE EFFECT OF PARAFFINIC SOLVENTS ON THE ONSET AND BULK PRECIPITATION OF ASPHALTENES

ABSTRACT

Asphaltene onset concentration and bulk deposition were measured for a typical live reservoir oil titrated with n-C₆H₁₄, n-C₅H₁₂, n-C₄H₁₀, C₃H₈, C₂H₆, CH₄ and CO₂ at 100° C (212 ° F) and 29.9 MPa (4340 psia). The concentration of titrant at asphaltene onset was observed to decrease approximately in a linear fashion with decreasing molecular weight of the paraffinic solvent; CH₄ did not induce any asphaltene precipitation. Bulk deposition experiments were performed using a solvent: oil volume ratio of 10:1; the results indicated that the weight percent of asphaltenes precipitated increased exponentially with decreasing molecular weight of the paraffinic solvents. More importantly, the asphaltene molecular weight showed a maximum for n-C₄H₁₀ precipitated asphaltenes. Possible explanations for this unusual result are presented.     

Modeling of Coprecipitation of Resin and Asphaltene in Crude Oil by Association Equation of State

Abstract

Resin content is an effective parameter that has adverse effect on precipitation of asphaltene in crude oil. Fluctuations in temperature, pressure, or oil composition disturb the chemical equilibrium in a reservoir, which results in coprecipitation of resin and asphaltene. In this work, coprecipitation of resin and asphaltene has been modeled using an association equation of state (AEOS) in which asphaltene and resin are considered associate components of oil. According to association fluid theory, the total compressibility factor is assumed to be the sum of physical and chemical compressibility factors. Liquid–liquid and liquid–vapor equilibrium calculations are accomplished with the assumption that asphaltene and resin do not contribute in the vapor phase. Comparison of experimental asphaltene precipitation with that obtained from the model developed proves the acceptability of the proposed model.     

FLOCCULATION OF ASPHALTENES AT HIGH PRESSURE. II. CALCULATION OF THE ONSET OF FLOCCULATION

The influence of pressure on the onset of flocculation of asphaltenes was calculated in the region from 1 to 300 bar and from 50 to 100°C. These calculations are the counterpart to our experimental data which, recently, have been reported in part 1 of an equally titled article [9]. As gas component methane and as precipitant i-octane were used. The asphaltene flocculation was considered to be a liquid-liquid equilibrium. For modelling the van der Waals equation of state (vdW-EOS) in the framework of continuous thermodynamics was applied. The composition of the crude oil was described by a continuous distribution function with respect to the solubility parameter δ of the Scatchard-Hildebrand theory. Within the distribution the asphaltenes represent the species with the highest δ-values. For oils with a very low content of asphaltenes the model developed describes the experimental flocculation data reasonably. In accordance to the experimental data the model predicts that, in the considered pressure range, without addition of i-octane asphaltene flocculation does not occur. However, on contrary to the experimental results, the model predicts the asphaltenes to show a higher flocculation tendency with increasing asphaltene content of the crude oil. For very high asphaltene contents the model even completely fails. Probably, the reason of this lack is the disregarding of asphaltene association.     

The Effects of Asphaltene Deposition on Unconsolidated Porous Media Properties during Miscible Natural Gas Flooding

Abstract

The deposition of asphaltenes in porous media is a complex phenomenon, which needs to be investigated under dynamic flowing conditions. Here, the likelihood of asphaltene deposition problems during dynamic displacement of dead oil by natural gas in unconsolidated porous media is experimentally inspected. Dynamic experiments showed a considerable increase in asphaltene deposition in the unconsolidated matrix during natural gas injection. The results show that increase in asphaltene deposition leads to pore plugging, porosity, and absolute permeability reduction of the porous media. Irreducible water measurements show that natural gas-induced asphaltenes change the sandstone wettability to oil-wet.     

Hydrotreating of Furrial Crude Oil Using NiMoS Supported on Alumina: Study of the Asphaltene and Their Fractions in Cyclohexane

Abstract

The Furrial crude oil originated in northern Monagas State. This shows problems such as the colloidal instability of the asphaltenes fraction present in them, causing its precipitation. This work is oriented to achieve an interpretation of the colloidal behavior of the asphaltenes through the study of the effect of the hydrotreating reactions (HDT) on the asphaltenes of the Furrial crude oil, using NiMoS/γ-Al₂O₃ as a catalyst. The results obtained after HDT reactions were analyzed to know the percentage of asphaltene and their fractions in cyclohexane, the measurement of flocculation thresholds and molecular weights by the VPO technique, and ¹³C NMR as well as the determination of the total sulfur content. Appreciable changes on the asphaltene of the Furrial crude oil and its fractions in cyclohexane after HDT, under conditions used, were observed. In general terms, the amount of asphaltene diminished and the percentage of distribution for insoluble fraction in cyclohexane (IFC) and for soluble fraction in cyclohexane (SFC) was affected causing an increase in the stability of the asphaltene. The asphaltene and IFC were observed to be a pronounced variation of the molecular weight average in number, in comparison with SFC. ¹³C NMR spectra indicate that the hydrotreated asphaltene shows structural change, and IFC presents a variation of the percentage of sulfur minor in comparison to SFC.     

PHYSICAL AND CHEMICAL CHARACTERIZATIONS OF ASPHALTENES FROM DIFFERENT SOURCES

ABSTRACT

Asphaltenes and resins are two of the several, but important, heavy organics present in petroleum fluids. Asphaltenes are operationally defined as the non-colatile and polar fraction of petroleum that is insoluble in n-alkanes (i.e., n-pentane). Conversely resins are defined as the non-colatile and polar fraction of petroleum that is soluble in n-alkanes (i.e., n-pentane), and aromatic solvents (i.e., toluene), and insoluble in ethyl acetate. A commonly accepted view in the petroleum chemistry is that crude oil asphaltenes form micelles which are stabilized by adsorbed resins kept in solution by aromatics. Two key parameters that control the stability of asphaltene micelles in a crude oil are the ratio of aromatics to saturates and that of resins to asphaltenes. When these ratios decrease, asphaltene micelles will coalesce and form larger aggregates. The precipitation of asphaltene aggregates can cause problems such as reservoir plugging and wettability reversal.     

REVERSIBILITY AND INHIBITION OF ASPHALTENE PRECIPITATION IN BRAZILIAN CRUDE OILS

ABSTRACT

A fundamental understanding of the aggregation and precipitation of asphaltenes in petroleum crudes is important for the development of preventive and curative measures for the potential problem of asphaltene deposition occurring during production, transport and refining operations. The question of reversibility of asphaltene precipitation, yet a controversial issue, is crucial for a clear and unequivocal understanding of the precipitation phenomenon, development of mathematical models that describe the behavior of asphaltenes in petroleum fluids, and the design of inhibitors. In this work, the behavior of precipitated asphaltenes in Brazilian crude tank oil samples following flocculant removal and gradual addition of fresh oil was investigated. The results obtained revealed a re-dissolution of precipitated asphaltene particles following flocculant removal and oil addition. On the inhibition of asphaltene precipitation, the capacity of a number of surfactants and block copolymers to inhibit asphaltene precipitation and deposition was also examined. Ethoxylated Nonylphenols and Hexadecyl Trimethyl Ammonium Bromide displayed highest capacity in the inhibition of asphaltene deposition.     

Kinetic Study on the Hydrocracking Reaction of Vacuum Residue Using a Lumping Model

Abstract

Based on the experimental hydrocracking of vacuum residue, a kinetic study using a lumping model was carried out to gain insight into the characteristics of catalytic reactions. The lumped species were the saturates, aromatics, resins, and asphaltenes (SARA) constituents in the residue (798 K⁺) fraction and gas, naphtha, kerosene, gas oil, vacuum gas oil, and coke in the products. The pyrite reaction favoring hydrocracking to lighter products was more temperature-dependent than that using a mixture of pyrite and active carbon. The kinetic study showed that the addition of active carbon to pyrite limited the transformation of resins to asphaltenes.     

PEPTIZATION AND COAGULATION OF ASPHALTENES IN APOLAR MEDIA USING OIL-SOLUBLE POLYMERS

ABSTRACT

The asphaltene fraction of crude oil contains a variety of acidic and basic functional groups. During oil production and transportation, changes in temperature, pressure or oil composition can cause asphaltenes to precipitate out crude oil through the flocculation among these polar functional groups. In this study, two types of oil-soluble polymers, dodecylphenolic resin and poly (octadecene maleic anhydride), were synthesized and used to prevent asphaltenes from flocculating in heptane media through the acid-base interactions with asphaltenes. The experimental results indicate that these polymers can associate with asphaltenes to either inhibit or delay the growth of asphaltene aggregates in alkane media. However, multiple polar groups on a polymer molecule make it possible to associate with more than one asphaltene molecule, resulting in the hetero-coagulation between asphaltenes and polymers. It was found that the size of the asphaltene-polymer aggregates was strongly affected by the polymer-to-asphaltene weight (or number) ratio. At low polymer-to-asphaltene weight ratios, asphaltenes keep flocculating with themselves and with polymers until the floes precipitate out of solution. On the other hand, at high polymer-to-asphaltene weight ratios, asphaltene-polymer aggregates peptized by the extra polymer molecules can remain fairly stable in the solution.