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.     

Asphaltene Precipitation Prediction Using a Micellization Model Based on Experimental Data

Abstract

Thermodynamic modeling is a promising tool for prediction of asphaltene precipitation. However, most thermodynamic models do not consider the micellar nature of petroleum fluids and the interaction between asphaltene-resin molecules. In addition, the accuracy of the predictions from the Peng-Robinson equation of state (PR-EOS) depends on the mixing rule. In this work, a thermodynamic framework based on the micellization model is used to describe the structure of asphaltene micelles as well as asphaltene precipitation in crude oil and the Wong and Sandler (1992) Wong, D. S. H. and Sandler, S. I. 1992. A theoretically correct mixing rule for cubic equations of state. AIChE J., 38: 671–680. [Crossref], [Web of Science ®] , [Google Scholar] mixing rule also is applied. The results show that the model predictions are matched well with the generated data for two Iranian crude oils.     

Investigation of Asphaltene Precipitation Process for Kuwaiti Reservoir

Abstract

As part of an Enhanced Oil Recovery (EOR) research program, Asphalting precipitation processes were investigated for a Kuwaiti dead oil sample using different hydrocarbons and carbon dioxide as precipitants at the ambient and high pressure of 3000 psig conditions. The hydrocarbons used as precipitants were ethane (C2), propane (C3), butane (C4), normal pentane (n-C5), normal hexane (n-C6), and normal heptane (n-C7). The equipment used for this investigation was a mercury-free, variable volume, fully visual JEFRI-DBR PVT system with laser light scattering. The minimum critical value of precipitants concentration for the oil sample has been identified at the ambient and high-pressure conditions for each precipitant. Our investigation has revealed that for this oil sample the most powerful asphaltene precipitant were CO₂ followed by C2, C3, C4, n-C5, n-C6, and n-C7. Moreover, the effect of pressure and temperature on the asphaltene precipitation has been investigated experimentally for CO₂, n-C5, n-C6, and n-C7. The precipitation and redissolution of asphaltene upon the addition and removal of CO₂ and light alkanes (C2–C4), at 3000 psig and ambient temperatures, have shown evidence of reversibility of asphaltene precipitation. A comprehensive fluid characterization analysis for the oil sample has been performed including, physical properties of crude oil, compositional, molecular weight (Mw), and SARA analyses. Advanced analytical techniques such as 1H and 13C NMR and IR spectrometers have been utilized to investigate the molecular structure of the asphaltene for this sample. It was concluded that the asphaltene molecules for this oil contain 120 total aromatic carbons with 42 aromatic rings, 114 naphthenic rings, and 5–7 sets of condensed aromatic rings.     

A Compositional Reservoir Simulation and Experimental Investigation of Asphaltene Onset Pressure

Asphaltene precipitation is one of the most common problems in many reservoirs and may lead to many safeties operational issues which affects on oil recovery; therefore, identifying start of asphaltene activation is known as a key parameter to control production efficiency. This study includes predicting onset pressure with Multiphase Flash test and compare its result with experimental data generated by Asphaltene Static Apparatus. Safety pressure was obtained by performing Multiphase Flash test for each component. In order to prevent adsorption, mechanical entrapment, and blockage, reservoir pressure must be higher than this safety pressure. SARA test is widely used to identify the fraction of crude oil that affect the asphaltene stability. IP143 standard test was used to measure precipitated asphaltene. Natural depletion test was designed at four steps, including 4400, 3000, 1550, and 1020 Psia and reservoir temperature is 205°F. It was seen that with decreasing pressure from reservoir pressure to saturation pressure asphaltene precipitation from PVT cell was increased and at pressures below saturation pressure with pressure reduction, asphaltene precipitations was decreased. Also it was concluded that above saturation pressure solubility model is dominant and below saturation pressure colloidal model is dominant. The results of IP143 show that initial content of asphaltene are 12.8%. SARA test result shows this kind of fluid located at unstable asphaltene precipitation region. Comparison of safety pressure between Multiphase Flash test and experimental data are investigated and discussed. Onset pressure of 18000 Pisa was obtained from Multiphase Flash test, which is in good agreement with experimental result.     

An Experimental Investigation of Asphaltene Precipitation During Natural Production of Heavy and Light Oil Reservoirs: The Role of Pressure and Temperature

Abstract

Many oil reservoirs encounter asphaltene precipitation as a major problem during natural production. In spite of numerous experimental studies, the effect of temperature on asphaltene precipitation during pressure depletion at reservoir conditions is still obscure in the literature. To study their asphaltene precipitation behavior at different temperatures, two Iranian light and heavy live oil samples were selected. First, different screening criteria were applied to evaluate asphaltene instability of the selected reservoirs using pressure, volume, and temperature data. Then, a high pressure, high temperature filtration (HPHT) setup was designed to investigate the asphaltene precipitation behavior of the crude samples throughout the pressure depletion process. The performed HPHT tests at different temperature levels provided valuable data and illuminated the role of temperature on precipitation. In the final stage, the obtained data were fed into a commercial simulator for modeling and predicting purposes of asphaltene precipitation at different conditions. The results of the instability analysis illustrated precipitation possibilities for both reservoirs which are in agreement with the oil field observations. It is observed from experimental results that by increasing the temperature, the amount of precipitated asphaltene in light oil will increase, although it decreases precipitation for the heavy crude. The role of temperature is shown to be more significant for the light crude and more illuminated at lower pressures for both crude oils. The results of thermodynamic modeling proved reliable applicability of the software for predicting asphaltene precipitation under pressure depletion conditions. This study attempts to reveal the complicated role of temperature changes on asphaltene precipitation behavior for different reservoir crudes during natural production.     

Experimental and Modelling Investigations of Asphaltene Precipitation During Pressure Depletion and Gas Injection Operations

Asphaltene precipitation problems manifest themselves in different stages of oil reservoirs production. Experimental and modeling investigations are, therefore, employed as promising tools to assist in predictions of asphaltene precipitation problems and selection of proper production facilities. This study concerns experimental and modeling investigations of asphaltene precipitation during natural production and gas injection operations for a heavy Iranian crude oil at reservoir conditions. First, with design and performance of high pressure–high temperature experiments, asphaltene precipitation behavior is comprehensively investigated; the effects of pressure and temperature are fully studied during pressure depletion tests and the role of injection gas composition on precipitation is described in gas injection experiments. In the next stage, the obtained experimental results are fed into a commercial simulator to develop the asphaltene precipitation model. The results for the pressure depletion experiments indicate that the maximum amount of asphaltene precipitation takes place at fluid bubble point pressure. Increase in the temperature, as seen, causes to reduce the amount of precipitation for the entire range of pressures. For gas injection experiments, the onset of precipitation for CO₂, associated, and N₂ gases takes place at around 0.20, 0.28, and 0.50 gas to mixture mole ratios, respectively. Carbon dioxide shows the highest asphaltene precipitation values and nitrogen has the lowest amounts for the whole range of gas mole fractions. Finally, the results for modeling indicate successful asphaltene precipitation predictions for both pressure depletion and gas injection processes.     

An Investigation of Asphaltene Precipitation During Natural Production and the CO2 Injection Process

Some of Iranian oil reservoirs suffer from operational problems due to asphaltene precipitation during natural depletion, so widely investigation on asphaltene precipitation is necessary for these reservoirs. In this study, a reservoir that is candidate for CO₂ gas injection process is selected to investigate asphaltene precipitation with and without CO₂ injection. In this case, asphaltene precipitation is monitored at various pressures and reservoir temperature. Then, a series of experiments are carried out to evaluate the amount of precipitated asphaltene by injection different molar concentrations (25%, 50%, and 75%) of CO₂. The results show that during primary depletion the amount of precipitated asphaltene increases with pressure reduction until bubble point pressure. Below the bubble point the process is reversed (i.e., the amount of precipitated asphaltene at bubble point pressure is maximum). The behavior of asphaltene precipitation versus pressure for different concentrations of CO₂ is similar to primary depletion. Asphaltene precipitation increases with CO₂ concentration at each pressure step. In the modeling part, solid model and Peng-Robinson equation of state are employed which show a good match with experimental results.     

The Thermodynamic Modeling of Asphaltene Precipitation Equilibrium during the Natural Production of Crude Oil: The Role of Pressure and Temperature

Asphaltene precipitation is a sophisticated issue in the upstream oil industry, worldwide, and has detrimental effect on a verity of production processes; it damages the properties of the reservoir and causes an unfavorable and significant decrease in oil production. In spite of numerous studies to predict asphaltene behavior, the effect of temperature on asphaltene precipitation during pressure depletion at reservoir conditions is still obscure in the literature. In this study the PVT data as well as experimental data of asphaltene precipitation at reservoir conditions of an Iranian light oil samples is used, and the asphaltene precipitation and deposition envelops (APE and ADE) of the oil are developed using solid thermodynamic modeling.     

应用新型油溶性两性物质抑制巴西原油中的沥青质沉淀

原油中重质组分沥青质和胶质导致原油生产中出现许多严重问题,包括油层、油井和输油管线设备中有机沉积物的形成,大大增加了生产作业成本.原油中沥青质性状复杂并随温度、压力和原油组分的变化而变化,在生产过程中,会导致沥青质组分沉积.解决方法有机械清除沉积物、溶剂冲洗以及用分散剂处理.可溶性两性油的应用为沉积物处理提供了最经济实用的解决方法.本研究采用三种巴西原油,为大量的新型化学添加剂对沥青质沉淀的抑制能力进行了检测.低相对分子质量的乙氧基壬基苯酚、植物油(椰子精华油、甜杏仁油、苦油树油、檀木油)以及有机酸(亚油酸、辛酸和棕榈酸)对沥青质沉淀的抑制最有效.评价了一些添加剂在脂肪族溶剂中对两种沥青质沉积物的溶解能力.十二烷基苯磺酸盐表现出明显的增溶作用,证实了本技术中酸碱相互作用的重要性.结果显示,脂肪族溶剂中沥青质的溶解/分散机理与原油中沥青质沉淀的抑制机理截然不同.     

The Effect of Temperature and Pressure on the Reversibility of Asphaltene Precipitation

A lot of hindrances are seen in petroleum operation, production, and transportation as a results of factors that related to asphaltene precipitation. It has great importance to investigate the reversibility of asphaltene precipitation under changes of effective factors on thermodynamic conditions such as pressure, temperature, and composition. In the present work the reversibility of asphaltene precipitation under changes of pressure and temperature was investigated for two kind of Iranian heavy oil. The stability test shows these samples are located at unstable region in aspect of asphaltene precipitation. The experimental procedure includes two parts, (a) decreasing pressure from initial reservoir pressure to near saturation pressure and surveying asphaltene content hysteresis with redissolution process at reservoir temperature, and (b) investigation of precipitated asphaltene in both precipitation and redissolution processes at different temperature and reservoir pressure. At each step IP143 standard test was used to measure precipitated asphaltene. It was concluded that above bubble point pressure, asphaltene precipitation is nearly reversible with respect to pressure for both samples and it was partially reversible with respect to the temperature for sample A, and accordingly pressurizing is acceptable method for solving the problem in both heavy asphaltenic crude oil samples and increasing temperature is acceptable method for solving asphaltene problem in crude oil sample A. Also density measurement of flashed oil confirmed that there is a little hysteresis in asphaltene content during redissolution and precipitation processes.     

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.     

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.     

Developing a Scaling Equation as a Function of Pressure and Temperature to Determine the Amount of Asphaltene Precipitation

A simple and applicable scaling equation as a function of pressure, temperature, molecular weight, dilution ratio (solvent), and weight percent of precipitated asphaltene has been developed. This equation can be used to determine the weight percent of precipitated asphaltene in the presence of difference precipitants (solvents) and the amount of solvent at onset point. Since increasing the pressure of crude oil decreases the amount of asphaltene precipitation, the effect of reservoir pressure has been taken into account in developing this equation. The results obtained by using this equation are substantially different and more accurate from other developed scaling equations for asphaltene precipitation. By considering the effect of reservoir pressure in developing the scaling equation and application of a genetic algorithm, the unknown parameters of the scaling equation are simultaneously and without any reservation obtained. The most important application of this unique equation is in the determination of critical point of asphaltene precipitation, known as onset point, and asphaltene precipitation in gas injection operations for enhanced oil recovery. The results predicted using the scaling equations are compared with the authors' experimental and literature precipitation data and it is shown that they are in good agreement with our experimental data. The scaling equation can be used in the design of gas-injected reservoir to prevent precipitation of the asphaltene aggregates in the reservoir.     

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.     

Prediction of Asphaltene Precipitation in Live and Tank Crude Oil Using Gaussian Process Regression

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. Since the nature of asphaltene solubility is yet unknown and several unmodeled dynamics are hidden in the original systems, the existing models may fail in prediction the asphaltene precipitation in crude oil systems. The authors developed some Gaussian process regression models to predict asphaltene precipitation in crude oil systems based on different subsets of properties and components of crude oil. Using feature selection techniques they found some subsets of properties of crude oil that are more predictive of asphaltene precipitation. Then they developed prediction models based on selected feature sets. Results of this research indicate that the proposed predictive models can successfully predict and model asphaltene precipitation in tank and live crude oils with good accuracy.     

The Role of Asphaltene Solubility and Chemical Composition on Asphaltene Aggregation

Abstract

Asphaltenes from four different crude oils (Arab Heavy, B6, Canadon Seco, and Hondo) were fractionated in mixtures of heptane and toluene and analyzed by small angle neutron scattering (SANS). Fractionation appeared to concentrate the most polar species into the least soluble sub-fraction as indicated by elemental analysis. SANS results indicated a wide spectrum of asphaltene aggregate sizes and molecular weights; however, the less soluble (more polar) fraction contributed the majority of the species responsible for asphaltene aggregation in solution. This more polar, less soluble fraction is likely the major cause for many petroleum production problems such as deposition and water-in-oil emulsion stabilization. A comparison of molecular weight and aggregate size indicated that asphaltenes formed fractal aggregates in solution with dimensions between 1.7 and 2.1. This was consistent with the “archipelago” model of asphaltene structure. Resins were shown to effectively solvate asphaltene aggregates as observed by an increase in asphaltene solubility, reduction in aggregate size and molecular weight, and an increase in the fractal dimension to ? 3.