Systematic Investigation of Asphaltene Deposition in the Wellbore and Near-Wellbore Region of a Deepwater Oil Reservoir under Gas Injection. Part 2: Computational Fluid Dynamics Modeling of Asphaltene Deposition

Babu, Narmadha Rajan; Lin, Pei-Hsuan; Abutaqiya, Mohammed I. L.; Sisco, Caleb J.; Wang, Jianxin; Vargas, Francisco M.

doi:10.1021/acs.energyfuels.8b03239

Cited by 16 publications

(19 citation statements)

References 35 publications

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“…The most successful models for predicting asphaltene precipitation accurately have come from the noncubic SAFT-based models: perturbed-chain SAFT (PC-SAFT) and CPA. The models based on PC-SAFT typically exclude the association term ,,− and model the asphaltene molecule as an aggregate of asphaltene primary particles, while models based on CPA include the association term − and model the asphaltene molecule in its primary particle form. Both PC-SAFT (without association) and CPA (with association) have shown considerable success in modeling asphaltene precipitation.…”

Section: Introductionmentioning

confidence: 99%

Accurate Modeling of Asphaltene Onset Pressure in Crude Oils Under Gas Injection Using Peng–Robinson Equation of State

Abutaqiya¹,

Sisco²,

Khemka

et al. 2020

Energy Fuels

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Asphaltene precipitation is considered a precursor of the plugging of oil wells and subsurface equipment and is a topic of continuous interest among companies and academic institutions. Numerous models to predict asphaltene precipitation at reservoir conditions have emerged over the years, and some have been dropped for several reasons. One particular case is the utilization of cubic equations of state such as Peng–Robinson (PR) and Soave–Redlich–Kwong (SRK), which although are relatively simple to code and utilize, have not been as effective in predicting asphaltene precipitation as compared to other models such as the perturbed chain version of the statistical associating fluid theory equation of state (PC-SAFT EOS). However, we have found that after improving the crude oil characterization procedure to obtain a proper set of simulation parameters from the available experimental data, the cubic equation of state can show excellent predictive capabilities in modeling asphaltene onset pressure under gas injection. In this work, we develop a characterization methodology based on the contents of Saturates–Aromatics–Resins–Asphaltenes (SARA) that can be used with PR EOS. Several case studies with published data from six crude oils are conducted to assess the predictive capability of the new approach in modeling asphaltene onset pressure under gas injection. Comparisons are made with PC-SAFT EOS to highlight the advantages and disadvantages of each model. Also, the modeling approach is tested against high-pressure and high-temperature data from four wells from the Middle East that have not been previously published in the literature. The results indicate that PR EOS yields results that are at least as good as those obtained from PC-SAFT in predicting the onset of asphaltene precipitation in crude oil under various amounts and types of gas injection.

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Section: Introductionmentioning

confidence: 99%

Accurate Modeling of Asphaltene Onset Pressure in Crude Oils Under Gas Injection Using Peng–Robinson Equation of State

Abutaqiya¹,

Sisco²,

Khemka

et al. 2020

Energy Fuels

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“…A modified version of the polydisperse asphaltene characterization technique proposed by Tavakkoli et al is implemented to account for the high amounts of resins and asphaltenes in crude oil C2. The modeling results obtained in this work are used as input to the deposition simulator (second paper of this series) to provide thermodynamic variables as a function of the pressure, temperature, and composition along the production profile.…”

Section: Introductionmentioning

confidence: 99%

Systematic Investigation of Asphaltene Deposition in the Wellbore and Near-Wellbore Region of a Deepwater Oil Reservoir Under Gas Injection. Part 1: Thermodynamic Modeling of the Phase Behavior of Polydisperse Asphaltenes

et al. 2019

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Thermodynamic modeling is conducted for a high-asphaltene, high-resin crude oil produced from a deepwater reservoir using the perturbed-chain statistical associating fluid theory (PC-SAFT). The asphaltenes are characterized as a polydisperse fraction following a three-parameter Γ distribution function with resins included as the lightest cut of the asphaltene distribution. Modeling results with 55 mol % injection indicate that the driving force of precipitation is sufficiently large that a significant amount of non-asphaltene components co-precipitate with asphaltenes. As pressure decreases from the upper asphaltene onset pressure (UAOP) to the bubble pressure (BP), the amount (by weight) of the asphaltene-rich phase surpasses the amount of the asphaltene-lean phase. Interestingly, as pressure decreases below the BP, the asphaltene-lean phase dissolves into the asphaltene-rich phase until the pressure reaches the lower onset pressure, where the lean phase is completely dissolved. As a result of the high driving force of precipitation, all asphaltenes precipitate out of solution before the BP and significant amounts of the other pseudo-fractions also co-precipitate. This causes the composition of asphaltenes in the asphaltene-rich phase to decrease as the pressure decreases, and the asphaltene composition in the asphaltene-rich phase at the upper onset is 23.6 wt %, which is uncommonly low for a precipitating phase. Experimental images from high-pressure microscopy show that the shape of the formed asphaltene-rich phase changes from a rigid solid-like structure near the UAOP to a soft liquid-like structure as the pressure decreases. This indicates a decrease in asphaltene composition during depressurization, similar to the simulation results produced by PC-SAFT. A sensitivity analysis is performed to evaluate the assumption of poly- and monodisperse asphaltenes from a modeling perspective and the effect of the Γ distribution parameters.

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“…Other factors, such as adequate mixing of the oil and precipitant, deposition before the capillary, end effects, scale-up, and the potential issues with using pressure drop in determining the deposition profile, as discussed in section , should be considered for a deposition test. Furthermore, deposition simulators have been developed to scale up the deposition profiles inside a capillary or packed bed to the fluid flow conditions in wellbores. ,, …”

Section: Discussionmentioning

confidence: 99%

Review of the Current Laboratory Methods To Select Asphaltene Inhibitors

Enayat¹,

Tavakkoli²,

Yen³

et al. 2020

Energy Fuels

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Asphaltene deposition is a long-standing problem that threatens the uninterrupted production of crude oil. Unlike other flow assurance problems, downhole asphaltene deposition is not well-understood partly as a result of the complexity and diversity of the asphaltene chemical structure. Continuous downhole asphaltene inhibitor injection is one of the preventive strategies used to mitigate the asphaltene deposition problem. Such chemicals that are injected in low dosage are screened in the laboratory before field implementation. Over the last 20 years, various asphaltene testing methods have been proposed for product selection and research and development. Still, the lack of lab to field correlation remains a challenge for the operators, service providers, and chemical companies. Therefore, the search for robust, rapid, and reproducible lab screening methods for asphaltene inhibitors is ongoing. This review summarizes lab evaluation techniques adopted by different researchers, their governing principles, and the pros and cons. It may serve as a guide for adopting appropriate strategies and pursuing further investigations on the lessons learned. The hope is that, with a more in-depth understanding of the current methodologies, a better workflow can be designed to select the proper asphaltene control products for field implementation.

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Systematic Investigation of Asphaltene Deposition in the Wellbore and Near-Wellbore Region of a Deepwater Oil Reservoir under Gas Injection. Part 2: Computational Fluid Dynamics Modeling of Asphaltene Deposition

Cited by 16 publications

References 35 publications

Accurate Modeling of Asphaltene Onset Pressure in Crude Oils Under Gas Injection Using Peng–Robinson Equation of State

Accurate Modeling of Asphaltene Onset Pressure in Crude Oils Under Gas Injection Using Peng–Robinson Equation of State

Systematic Investigation of Asphaltene Deposition in the Wellbore and Near-Wellbore Region of a Deepwater Oil Reservoir Under Gas Injection. Part 1: Thermodynamic Modeling of the Phase Behavior of Polydisperse Asphaltenes

Review of the Current Laboratory Methods To Select Asphaltene Inhibitors

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