Modeling Of Asphaltene Precipitation Research Articles

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.

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.

Asphaltene Instability Trends to Predict Asphaltene Precipitation Onset Pressure: Constrained for Light and Heavy Crude Oils

The study investigates asphaltene instability trends (ASISTs), originally developed by New Mexico Tech, for crude oils with different °API gravities and asphaltene contents. Different ratios of precipitants/solvents have been used to construct the ASISTs at ambient conditions. Results were extended to reservoir conditions using routine PVT data. The ASIST method was used to predict the asphaltene precipitation onset pressure at reservoir conditions. Due to the kinetic effect on asphaltene precipitation using the titration method, pressure depletion induced asphaltene precipitation experiments were carried out for the heavy oil sample. Results, using the modified Miller–Flory–Huggins model, were thermodynamically modeled to validate the ASIST observations and for finding an appropriate lag time that can correspond to reservoir conditions. Similar asphaltene precipitation and thermodynamic modeling were adapted from the literature for the light oil sample. Results suggest that, for the light oil sample, 5 hours is appropriate lag time to perform ASIST, which is in agreement with the reported trends; for the heavy oil sample, however, results suggest that even a 5 hours lag time is not short enough for the titration experiment so that it can be kinetically extended to asphaltene precipitation in the reservoir. We suggest a 1 hour lag time for the samples with low °API gravities and high asphaltene contents. More case studies from different crude oils from different geological settings are required to draw any rigid kinetic framework for the ASIST.

Asphaltene precipitation of titration data modeling through committee machine with stochastically optimized fuzzy logic and optimized neural network

Deposition of asphaltene during crude oil production is a challenging issue in oil industry which causes considerable loss of production efficiency as well as imposes negative impacts on production rates. Upon variation in pressure, temperature and crude oil composition, asphaltene begins to precipitate and deposits in reservoir rock and consequently causes formation damage owing to mechanisms of wettability alteration and pore throat blockage. In the present study a sophisticated method, called committee machine with optimized intelligent systems was utilized to predict the amount of asphaltene precipitation from experimental titration data. The committee machine is composed of optimized neural network and optimized fuzzy logic. Stochastic optimization of neural network and fuzzy logic by virtue of hybrid genetic algorithm-pattern search technique significantly enhances their efficiencies. The committee machine provides a further improvement in accuracy of final prediction through integrating optimized intelligent systems and consequent reaping of their benefits. The committee machine model was applied to experimental data reported in the open-source literature. It was observed that there was an acceptable agreement between experimental data and committee machine predicted values. Finally, performance of committee machine model was compared with other intelligent systems used for prediction of asphaltene precipitation. Results showed superiority of committee machine in asphaltene precipitation modeling to optimized neural network and optimized fuzzy logic.

Modeling and simulation of asphaltene precipitation by normal pressure depletion

Asphaltene precipitation is a serious and complex problem in all sections of the oil industry including oil production, transportation and processing. In this paper the analysis of several variables affecting asphaltene deposition by normal pressure depletion was carried out using the Hirschberg et al. (1984) model and experimental data from literature. The results show that the traditional Hirschberg et al. (1984) model can be applied successfully when modifications to the oil characterization, the calculation of oil and the asphaltene solubility parameter are made. We demonstrate that a quantitative representation of asphaltene deposited due to pressure depletion can be obtained with great precision using a minimum amount of experimental data. • Variables affect asphaltene deposition by normal pressure depletion was studied. • The Hirschberg et al. (1984) model was used together with experimental data. • The results show that the traditional model can be applied successfully with changes. • Oil characterization, oil and asphaltene solubility parameters need modifications.

Modeling asphaltene precipitation and flow behavior in the processes of CO2 flood for enhanced oil recovery

This paper focuses on the idea for enhancing oil recovery by CO2 injection into oil formation as well as reducing CO2 emission into the atmosphere. The mechanism of asphaltene flocculation during CO2 flooding in oil formations is analyzed and the regressive correlation between CO2 concentration and the amount of flocculated asphaltene for an oil sample is set up for coupling with the flow governing equations for the flocculated asphaltene transport in porous media. A three-dimensional multiphase mathematical model describing CO2 transport in oil reservoirs and asphaltene precipitation is presented for predicting CO2 flooding performances for enhanced oil recovery. The finite difference method and preconditioned conjugate gradient algorithm are used to solve the discrete nonlinear equation systems. A numerical simulation software is developed to study CO2 flooding performances and the effects of asphaltene precipitation on production behaviors. The numerical result indicates that water-cut decreases from initial 92.5% down to 40.3% after continuous CO2 injection for 10 years. It is also shown that 0.130 million tons of crude oil is displaced by CO2 injection in the 1 km2 of the reservoir within 10 years. Asphaltene precipitation leads to the decrease in permeability, and the decline in production rates.

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.

Phase Behavior Modeling of Asphaltene Precipitation for Heavy Crudes: A Promising Tool Along with Experimental Data

Thermodynamic modeling is known as a promising tool for phase behavior modeling of asphaltene precipitation under different conditions such as pressure depletion and CO2 injection. 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, while the oil and gas phases are modeled with an equation of state. The 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 an improved solid model has been developed and used for predicting asphaltene precipitation data for one of Iranian heavy crudes, under pressure depletion and CO2 injection conditions. A significant improvement has been observed in predicting the asphaltene precipitation data under gas injection conditions. Especially for the maximum value of asphaltene precipitation and for the trend of the curve after the peak point, good agreement was observed. For gas injection conditions, comparison of the thermodynamic micellization model and the improved solid model showed that the thermodynamic micellization model cannot predict the maximum of precipitation as well as the improved solid model. The non-isothermal improved solid model has been used for predicting asphaltene precipitation data under pressure depletion conditions. The pressure depletion tests were done at different levels of temperature and pressure, and the parameters of a non-isothermal model were tuned using three onset pressures at three different temperatures for the considered crude. The results showed that the model is highly sensitive to the amount of solid molar volume along with the interaction coefficient parameter between the asphaltene component and light hydrocarbon components. Using a non-isothermal improved solid model, the asphaltene phase envelope was developed. It has been revealed that at high temperatures, an increase in the temperature results in a lower amount of asphaltene precipitation and also it causes the convergence of lower and upper boundaries of the asphaltene phase envelope. This work illustrates successful application of a non-isothermal improved solid model for developing the asphaltene phase envelope of heavy crude which can be helpful for monitoring and controlling of asphaltene precipitation through the wellbore and surface facilities during heavy oil production.

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

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.

Prediction of asphaltene phase behavior in live oil with CPA equation of state

Asphaltene deposition mostly occurs when the thermodynamic conditions, like temperature, pressure, and oil composition of crude oil change. Therefore predicting asphaltene phase behavior through thermodynamic modeling may help to control its precipitation and reduce the associated problems. In this work, the Cubic-Plus-Association (CPA) equation of state was used to model asphaltene precipitation in live oil. This equation is based on a combination of a physical part and an association term. Through the past decades, CPA equation of state has been successfully applied to several complex mixtures of water, alcohols, glycols, organic acids, and hydrocarbons. As asphaltene molecules have an associative nature, which leads to form aggregation and finally precipitation, this equation can precisely predict the phase behavior of asphaltene and other heavy components in crude oil. In this work, the results of Cubic-Plus-Association equation of state were compared to experimental data of three live oils and the results of two other models. The results showed that CPA equation of state can predict asphaltene precipitation with good accuracy.

Experimental investigation and thermodynamic modeling of asphaltene precipitation

Asphaltene precipitation may occur during pressure depletion or gas injection processes in a reservoir. This phenomenon is an important problem during oil production, because it can result in formation damage and the plugging of wellbore and surface facilities. In this work, the precipitation of asphaltenes in an Iranian crude oil, under different pressures, is measured, using an experimental set up based on high-pressure isothermal expansion and also atmospheric titration. For the particular oil investigated, compositional data, precipitation phase diagrams, and bubble point and onset pressures are reported. Also, in this work, the Perturbed Chain form of the Statistical Associating Fluid Theory (PC-SAFT), the Solid model and the Flory-Huggins model are used, to correlate onset pressures and solvent ratios at the onset point of precipitation for different solvents. The results show that the PC-SAFT EOS correlates more accurately with the experimental results over a wide composition interval.

Phase behavior analysis of heavy oil containing asphaltene

A thermodynamic asphaltene precipitation model was developed based on the model of Nghiem et al. using the Peng-Robinson equation of state (PR EOS). This model calculates the amount of asphaltene precipitation based on changes in temperature and pressure. The effects of asphaltene precipitation on rock properties were investigated by observing changes in porosity and permeability. In this model, phase equilibrium was achieved by repeatedly reducing the amount of the asphaltene component, in contrast to the model of Qin et al. using the secant method. The results of this model were compared with experimental data of measured precipitation, based on changes in pressure, and agreed more closely than the model of Qin et al. Our results also confirmed that the maximum precipitation of asphaltene occurred at the bubble-point pressure. Thus, using the model, we verified that the precipitation pattern of asphaltene depended on temperature and pressure, and it is expected that changing patterns in reservoir productivity can be analyzed using asphaltene precipitation in heavy oil-containing asphaltene.

Modeling of asphaltene precipitation utilizing Association Equation of State

Precipitation of heavy organics, especially asphaltenes, causes many problems in oil industry. Thermodynamic models are used in order to realize the phase behavior of asphaltene deposition. In this study, the Association Equation of State (AEOS) model was developed for predicting asphaltene precipitation in crude oil. In addition to a physical term, a chemical term was used in this model to consider the association nature of asphaltene. The result of this model was compared to experimental data of three live oils and the results of other models. The results showed good accuracy of this model in comparison with the other models.

An Experimental Measurement and Modeling of Asphaltene Precipitation in Crude Oil

In this study, the amount of asphaltene precipitation produced by adding different ratios of n-alkanes (n-pentane, hexane, n-heptane, n-nonane) into two dead oil samples from Iranian oil fields has been measured. The obtained results were compatible with the results of other researchers. Meanwhile, the reversibility of asphaltene precipitation process has been investigated as an important subject in this phenomenon. Then, by using the experimental data and optimization, the scaling equation's coefficients were calculated and then the amount of asphaltene precipitation and the onset point have been obtained by the scaling approach. Finally, the obtained results have been compared with the experimental data and the model's accuracy was approved.

Prediction of Asphaltene Precipitation During Solvent/CO2 Injection Conditions: A Comparative Study on Thermodynamic Micellization Model With a Different Characterization Approach and Solid Model

Summary There are different thermodynamic models that have been applied for modelling of asphaltene precipitation caused by various reasons, such as solvent/CO2 injection and pressure depletion. In this work, two computer codes based on two different asphaltene precipitation thermodynamic models--the first being the thermodynamic micellization model with a different characterization approach and the second being the solid model--have been developed and used for predicting asphaltene precipitation data reported in the literature as well as in the obtained data for Sarvak reservoir crude, which is one of the most potentially problematic Iranian heavy oil reserves under gas injection conditions. For the thermodynamic micellization model, a new approach was obtained by applying the characterization method taken from the thermodynamic solid model for oil component characterization. This new approach introduced a new matching parameter to the model, representing the interaction coefficients between asphaltene components and light hydrocarbon components, which resulted in a significant improvement in the thermodynamic micellization model predictions of asphaltene precipitation data under gas injection conditions. The model parameters obtained from a sensitivity analysis were applied in both thermodynamic models, and the experimental data of asphaltene precipitation were predicted. The asphaltene precipitation predictions from the solid model showed good agreement with the data taken under gas/solvent injection conditions. Especially for the trend of the titration curve after the peak point, reasonable agreements were observed which could rarely be found in the available literature. It has been observed that although the thermodynamic micellization model with a different characterization approach is more complex than the solid model, it is able to predict the trends of asphaltene precipitation curves for gas titration conditions reasonably well. Also, its predictions matched well with more experimental data points in comparison to the solid model predictions.

Application of a Regular Solution-Based Model to Asphaltene Precipitation from Live Oils

A previously developed regular solution approach was adapted to model asphaltene precipitation caused by compositional changes and depressurization. The model inputs are the mass fraction, molecular weight, density, and solubility parameters for each component. A Gulf of Mexico crude oil was characterized into components, and mass fractions were assigned on the basis of gas chromatographic and saturates, aromatics, resins, and asphaltenes (SARA) analysis. Densities for pentane plus and SARA fractions were obtained from published data. For lighter components, effective densities were determined from extrapolated n-alkane data. The density of the live oil from 80 to 120 °C and pressures from 10 to 100 MPa was predicted to within the error of the data assuming ideal mixing. Solubility parameters of each component were determined as a function of the temperature and pressure. The only unknown was the average molar mass of the asphaltene nano-aggregates in the oil, which was used to fit the measured precipitation onset pressure data. The model successfully predicted asphaltene yield data below the onset pressure for the live oil as well as yields for the dead oil diluted with n-heptane. The results indicate that a common characterization can be used to model both solvent- and pressure-induced precipitation. However, the pressure-induced precipitation is very sensitive to the average aggregate molar mass. Thus, the predictive capability of this approach is limited.

A NOVEL METHOD FOR MITIGATION OF ASPHALTENE DEPOSITION IN THE WELLSTRING

Asphaltene precipitation and deposition is a serious problem in many Iranian fields. The deposited asphaltene results in partial or total blockage of the wellbore and wellstring reducing or completely seizing oil production. This paper studies the asphaltene problem and mitigation methods in wellstring systematically. It presents new approach based on the combination of thermodynamic modeling of asphaltene precipitation with hydrodynamic well modeling. The developed model is capable to determine the asphaltene precipitation and deposition interval through the wellstring. Therefore, it could study the effect of hydrodynamic parameters such as wellhead pressure, well flow-rate and tubing size on the mitigation of asphaltene deposition. The conventional way to treat asphaltene deposition was through remediation which attacks the problem after it occurs. This model is capable to determine the severity of asphaltene deposition even before start of production. The model was applied to simulate the asphaltene precipitation in one of the south Iranian oil fields (Kupal) and important guidelines have been studied to mitigate the risks associated with asphaltene deposition. The results of modeling show that change in hydrodynamic condition could reduce and mitigate asphaltene deposition damage. But in some cases it would not be possible to prevent asphaltene precipitation completely. Therefore, the approach for flow assurance in those cases would be to change the well completion and inject asphaltene inhibitor, in order to prevent deposition of asphaltene flocculates.

An Applied and Efficient Model for Asphaltene Precipitation In Production and Miscible Gas Injection Processes

In this article the modeling results of asphaltene precipitation in some Iranian oil fields via a new and applicable polymer solution based model are presented. Application of polymer solution and lattice theory in this model leads to accurate prediction of the environmental parameters effects on asphaltene precipitation in oil fields. The involved parameters are pressure change through natural depletion; temperature change in fluid flow path during oil production; and composition change through either miscible gas injection or pressure and/or temperature variations. The adjustable parameters of the model are tuned via experimental data of asphaltene precipitation. The adjusted parameters can be used in the simulation section, which is the prediction of the effects of different parameters on amount of asphaltene deposition in oil mixtures. The experimental data in this work are the precipitation data in some Iranian live oils. The modified Flory-Huggins is re-modified by replacement of the entropy term in this model with the Miller's entropy term. The Peng-Robinson equation of state is also applied to vapor-liquid equilibrium calculations.

Measurement and Modeling of Asphaltene Precipitation from Crude Oil Blends

A previously developed regular solution model was adapted to predict the onset and amount of asphaltene precipitation from crude oil blends diluted with pure n-alkanes or a mixture of toluene and n-heptane. Tests were conducted on nine different crude oils, a gas oil, and their blends. Oils and blends were characterized in terms of SARA (saturates, aromatics, resins, and asphaltenes) fractions. The mass fraction of each SARA fraction in the blends was confirmed as a weight average of the respective fraction in constituent oils. Asphaltenes were subdivided into fractions based on the gamma function to account for the distribution of aggregates resulting from self-association. To model the asphaltene onset and yield, liquid−liquid equilibrium was assumed between a heavy (asphaltenic) and a light (nonasphaltenic) phase. The distribution of asphaltenes in unblended crude oils was determined by fitting its asphaltene yield data when diluted with n-heptane. The fitting parameter in the model was the average aggregation number of asphaltenes in the source oils. Two approaches were tested to calculate the distribution of asphaltenes in crude oil blends. In the first approach, asphaltenes were assumed to interact, and the final molar mass distribution was determined from gamma function using the average aggregation number of constituent oils. In the second approach, no interaction was assumed, and the final distribution was calculated as a sum of the individual distributions. It was found that the best predictions of the onset and yield data were obtained by using the second approach. The mass fraction of n-heptane required to initiate precipitation was predicted with an average absolute deviation of 0.53% or less in all cases.

Polymer solution and lattice theory applications for modeling of asphaltene precipitation in petroleum mixtures

Here asphaltene precipitation in petroleum reservoirs during natural depletion and miscible gas injection is modeled via two distinct and new methods (polymer solution and lattice theories). The first model is based on the polymer solution theory, which is a combination of Miller's combinatorial term with a modified residual term of the original Flory-Huggins theory. The second one is the application of the well-known Sanchez-Lacombe equation of state (SL EOS) to describe the phase behavior of asphaltene compounds in crude oil. The results of both models show an acceptable and good agreement between the real data (field and experimental) and these two models. As it can be seen from the obtained results of these two models, it seems application of the lattice or polymer solution theories (based on The Miller's combinatorial term) could give the better and more close to real data.