Mechanism of the reversibility of asphaltene precipitation in crude oil

Abedini, Ali; Ashoori, Siavash; Torabi, Farshid; Saki, Yaser; Dinarvand, Navid

doi:10.1016/j.petrol.2011.07.010

Cited by 30 publications

(34 citation statements)

References 11 publications

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“…Figure b compares the amount of the deposited asphaltenes obtained from all solvent runs for both 50 and 100 μm cases. Comparing the two pure solvents, pentane precipitated a larger amount of asphaltenes in the porous medium than heptane, in agreement with previous studies . The condensate sample, however, precipitated less asphaltenes than pentane and more than pure heptane.…”

Section: Resultssupporting

confidence: 91%

“…A series of experiments have been carried out to quantify the asphaltene precipitation and resulting formation damage during enhanced oil recovery processes. A high-temperature high-pressure PVT cell was used to study the effects hydrocarbon solvent dilution ratio, temperature, and pressure on the asphaltene onset and precipitation rate. − Slim tube apparatus was also employed to monitor the pressure fluctuation and flow turbulence as a result of asphaltene precipitation during solvent injection . In addition, high-pressure coreflooding has been applied to estimate permeability reduction as a result of asphaltene deposition during immiscible and miscible CO 2 injection processes. − Asphaltene deposition during the vapor extraction process with propane and butane has been measured using sand-packed physical models. , In addition, the roles of morphology and mineralogy of the rock were analyzed with regard to asphaltene deposition and associated reservoir damage .…”

Section: Introductionmentioning

confidence: 99%

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Asphaltene Deposition during Bitumen Extraction with Natural Gas Condensate and Naphtha

Abedini

Sharbatian

et al. 2018

Energy Fuels

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Solvent bitumen extraction processes are alternatives to thermal processes with potential for improved economic and environmental performance. However, solvent interaction with bitumen commonly results in in situ asphaltene precipitation and deposition, which can hinder flow and reduce the process efficiency. Successful implementation requires one to select a solvent that improves recovery with minimal flow assurance problems. The majority of candidate industrial solvents are in the form of mixtures containing a wide range of hydrocarbon fractions, further complicating the selection process. In this study, we quantify the pore-scale asphaltene deposition using two commonly available solvent mixtures, natural gas condensate and naphtha, using a microfluidic platform. The results are also compared with those of two typical pure solvents, n-pentane and n-heptane, with all cases evaluated with both 50 and 100 μm pore-throat spacing. The condensate produced more asphaltenes and pore-space damage than the naphtha and exhibited deposition dynamics similar to that of pentane and heptane. This similarity is due to the presence of a large amount of light hydrocarbon fractions in condensate (∼85 wt % of C5s–C7s) dictating the overall deposition dynamics. Naphtha, which contains heavier fractions (∼70 wt % of C8s–C11s) and aromatic/naphthenic components, generated less asphaltenes and exhibited a slower deposition rate, resulting in less pore damage and overall better performance.

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Section: Resultssupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Asphaltene Deposition during Bitumen Extraction with Natural Gas Condensate and Naphtha

Abedini

Sharbatian

et al. 2018

Energy Fuels

Self Cite

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“…Most researchers hold the point that the precipitated asphaltene is reversible, and lots of models proposed are based on that [33][34][35][36][37][38][39], such as Flory-Huggins solubility model, solid model, and dense-liquid model. However, few researchers think the precipitated asphaltene is irreversible, and models proposed by [40,41] are based on that. They considered that resins can hardly recovery once destroyed, so the stability of oil can never recovery once decreased.…”

Section: Introductionmentioning

confidence: 99%

Asphaltene Deposition during CO2 Flooding in Ultralow Permeability Reservoirs: A Case Study from Changqing Oil Field

Zou

et al. 2021

Geofluids

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Asphaltene deposition is a common phenomenon during CO2 flooding in ultralow permeability reservoirs. The deposited asphaltene occupies the pore volume and decreases permeability, resulting in serious formation damage and pore well productivity. It is urgent to investigate the asphaltene deposition mechanisms, adverse effects, and preventive measures. However, few asphaltene deposition investigations have been systematically conducted by now. In this research, the asphaltene precipitation mechanisms and adverse effects were comprehensively investigated by using experimental and numerical methods. To study the effects of pressure, asphaltene content, and temperature on asphaltene precipitation qualitatively and quantitatively, the microscope visible detection experiment and the PVT cell static experiment were firstly conducted. The adverse effects on porosity and permeability resulted from asphaltene deposition were also studied by the core flooding experiment. Secondly, simulation models of asphaltene precipitation and deposition were developed and validated by experimental data. Finally, a case study from Changqing oil field was presented to analyze the asphaltene deposition characteristic and preventive measures. The experimental results showed that the asphaltene precipitation increases with the increased pressure before reaching the minimum miscible pressure (MMP) and gets the peak value around the MMP, while decreases slowly. The asphaltene precipitation increases with the increased temperature and asphaltene content. The variation trend of adverse effects on porosity and permeability resulted from asphaltene deposition is similar to that of asphaltene precipitation under the influence of pressure, asphaltene content, and temperature. The case study shows that the water-altering-gas (WAG) with high injection rate suffers more serious asphaltene deposition compared with the WAG with low injection rate, for the asphaltene precipitation increases as the increased pressure before reaching the MMP. The CO2 continuous injection with high injection rate is the worst choice, for low sweep efficiency and the most severe formation damage. Thus, the WAG with optimal injection rate was proposed to maintain well productivity and to reduce formation damage resulted from asphaltene deposition during developing ultralow permeability reservoirs.

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“…Other techniques such as mechanical, chemical, bacterial, thermal, and ultrasonic treatments have been applied to clean deposited asphaltenes [32][33][34][35][36][37][38][39]. It should be noted that asphaltenes can reprecipitate due to changes in thermodynamic conditions such as pressure and temperature during production, which may render production uneconomical [40].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Based Methods for Asphaltenes Adsorption by Nanocomposites: Application of Group Method of Data Handling, Least Squares Support Vector Machine, and Artificial Neural Networks

et al. 2020

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Asphaltenes deposition is considered a serious production problem. The literature does not include enough comprehensive studies on adsorption phenomenon involved in asphaltenes deposition utilizing inhibitors. In addition, effective protocols on handling asphaltenes deposition are still lacking. In this study, three efficient artificial intelligent models including group method of data handling (GMDH), least squares support vector machine (LSSVM), and artificial neural network (ANN) are proposed for estimating asphaltenes adsorption onto NiO/SAPO-5, NiO/ZSM-5, and NiO/AlPO-5 nanocomposites based on a databank of 252 points. Variables influencing asphaltenes adsorption include pH, temperature, amount of nanocomposites over asphaltenes initial concentration (D/C0), and nanocomposites characteristics such as BET surface area and volume of micropores. The models are also optimized using nine optimization techniques, namely coupled simulated annealing (CSA), genetic algorithm (GA), Bayesian regularization (BR), scaled conjugate gradient (SCG), ant colony optimization (ACO), Levenberg–Marquardt (LM), imperialistic competitive algorithm (ICA), conjugate gradient with Fletcher-Reeves updates (CGF), and particle swarm optimization (PSO). According to the statistical analysis, the proposed RBF-ACO and LSSVM-CSA are the most accurate approaches that can predict asphaltenes adsorption with average absolute percent relative errors of 0.892% and 0.94%, respectively. The sensitivity analysis shows that temperature has the most impact on asphaltenes adsorption from model oil solutions.

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Mechanism of the reversibility of asphaltene precipitation in crude oil

Cited by 30 publications

References 11 publications

Asphaltene Deposition during Bitumen Extraction with Natural Gas Condensate and Naphtha

Asphaltene Deposition during Bitumen Extraction with Natural Gas Condensate and Naphtha

Asphaltene Deposition during CO2 Flooding in Ultralow Permeability Reservoirs: A Case Study from Changqing Oil Field

Artificial Intelligence Based Methods for Asphaltenes Adsorption by Nanocomposites: Application of Group Method of Data Handling, Least Squares Support Vector Machine, and Artificial Neural Networks

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