Effects of cation salinity on the dynamic interfacial tension and viscoelasticity of a water-oil system

Mahmoudvand, Mohsen; Javadi, Aliyar; Pourafshary, Peyman; Vatanparast, Hamid; Bahramian, Alireza

doi:10.1016/j.petrol.2021.108970

Cited by 27 publications

(24 citation statements)

References 53 publications

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“…It can be noticed that the experimental IFT profile tends to be decreased over time to reach a minimum value at the equilibrium stage and then remains constant with the interface aging time. Conceptually, the obtained dynamic behavior of IFT is believed to be caused by the adsorption kinetics of interfacially active molecules (i.e., CTAB) at liquid interfaces, where the adsorption kinetics of CTAB molecules can be controlled by mass transfer phenomena from the bulk in the nanofluid phase into an adsorbed state or vice versa . Between every two immiscible fluids, there are several variables to monitor the adsorption and desorption of the surfactant at the interface area as well as the diffusive movement .…”

Section: Results and Discussionmentioning

confidence: 99%

“…Conceptually, the obtained dynamic behavior of IFT is believed to be caused by the adsorption kinetics of interfacially active molecules (i.e., CTAB) at liquid interfaces, where the adsorption kinetics of CTAB molecules can be controlled by mass transfer phenomena from the bulk in the nanofluid phase into an adsorbed state or vice versa . Between every two immiscible fluids, there are several variables to monitor the adsorption and desorption of the surfactant at the interface area as well as the diffusive movement . Accordingly, it has been reported that the dynamic effect of the surfactant adsorption can be described by micellization equilibria and random sequential theory. , Conversely, other authors have proven that the reduction profile for the IFT, due to the presence of an active energy barrier, could be characterized by a simple exponential decay. , These models, even with simplicity, have failed in observing the mass transfer controlling mechanism, whether it is adsorption or diffusion, since both have been utilized loosely and interchangeably. , In detail, it has been reported that the controlling mechanism can be done during the transfer of surfactant molecules between the surface layer and the subsurface layer (adsorption) or it can be governed by the exchange of molecules between the subsurface and the bulk solution (diffusion along with occasional convection) .…”

Section: Results and Discussionmentioning

confidence: 99%

“…Despite many successful laboratory-scale and field testing, most of these surfactants have shown limitations in cost, stability, and use in harsh environments, i.e., high reservoir temperature, salinity, pH, and pressure that results in significant adsorption of the surfactant on the rock matrix. Therefore, high-performance surfactants are highly desirable. ,,,− , Anionic surfactants, for instance, have shown great potential and extensive applicability as surface-active agents at industrial scales, mainly because of their high stability, low cost, and limited adsorptive features toward reservoir rocks, except for carbonate reservoirs. − Certainly, the equivalent charges of anionic surfactants with rock surfaces in carbonate reservoirs have shown weak effectiveness for oil recovery …”

Section: Introductionmentioning

confidence: 99%

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Influence of CTAB-Grafted Faujasite Nanoparticles on the Dynamic Interfacial Tension of Oil/Water Systems

et al. 2022

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In this study, the dynamic IFT and dilational rheological behavior of fluids/nanofluids generated from cetyltrimethylammonium bromide (CTAB)-grafted faujasite (FAU)-based nanoparticles were investigated in toluene–fluid/nanofluid interfaces under various conditions. To generate each nanofluid, FAU nanoparticles were initially synthesized under ambient conditions and then grafted with various quantities of CTAB surfactants. Afterward, the surface properties (i.e., morphology, surface area, functionality, and stability) of the synthesized nanoparticles before and after grafting with a low concentration of CTAB molecules were investigated by an array of characterization methods (XRD, HRTEM, BET surface area, ζ-potential, and DLS). The results showed that our generated FAU-based nanofluids outperformed the nongrafted FAU and physically mixed CTAB with FAU-based nanofluids in terms of IFT reduction. Our experimental IFT data were fitted with a general diffusion model that allowed us to determine the oil–water (o/w) emulsification mechanism due to the presence of CTAB-grafted nanoparticles. Based on our investigation, we emphasized that the IFT reduction was limited with the loading of a substantial amount of surfactant molecules, indicating that bulky nanoparticles are adsorbed slowly on the o/w interface. On the other hand, increasing the salinity level by introducing an additional amount of NaCl led to enhancing the levels of IFT reduction since the dissociated ions forced more surfactant molecules to migrate toward the o/w interface. A considerable viscoelasticity enhancement was remarked in the presence of our CTAB-grafted nanofluids, indicating that the presence of FAU nanoparticles in the core of the grafted nanoparticles robustly allowed the adsorption of higher amounts of CTAB molecules at the interface with less aggregation. The findings of this study shed light on the applications of nanofluids generated from grafted nanoparticles at the o/w systems, which can significantly increase the oil production at the end of water flooding with more efficient emulsification with surfactant concentration lower than the critical micelle concentration (CMC).

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Section: Results and Discussionmentioning

confidence: 99%

Section: Results and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Influence of CTAB-Grafted Faujasite Nanoparticles on the Dynamic Interfacial Tension of Oil/Water Systems

et al. 2022

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“…First, an interfacial layer with high viscoelasticity is known to be formed between the aqueous and lipophilic phases. It has a resistance to compression, hindering the external particles from moving into the oil phase (Jeon & Hong, 2014; Mahmoudvand et al., 2021). High IFT ow leads to a stronger interfacial layer, which is difficult to be broken up by eddy kinetic energy.…”

Section: Discussionmentioning

confidence: 99%

Effects of Oil and Sediment Properties and Mixing States on the Formation and Settling of Oil‐Particle Aggregates

Xiong

et al. 2022

JGR Oceans

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Understanding the interaction of oil droplets with suspended particulate matter is important not only for sunken oil modeling but also for emergency response. In this study, the effects of mixing time and energy, oil type, and sediment concentration and size on the oil content, morphology, and density of oil‐particle aggregates (OPAs) were investigated through a series of wave tank experiments and sedimentation tests. The results showed that loads of oil and particles in OPAs are high under high mixing energy. Under turbulent hydrodynamics, moderately viscous oils form OPAs more easily and sink rapidly to the seabed. Moreover, an increase in the density and oil capacity of OPAs is observed at a higher sediment concentration. More oil is captured by finer sediments. However, the corresponding oil‐sediment aggregates generally show lower density compared with those formed by larger sediments. The oil‐seawater interfacial tension (IFTow) diminishes with the addition of sediments, further promoting oil dispersion. The increasing duration and mixing energy produce more flake OPAs and multiple‐droplet OPAs. Oil droplets tend to be surrounded by several small particles but attach to the surface of large particles. Regardless of oil type and sediment concentration, the structure of OPAs does not change significantly.

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“…Similarly, many other experimental works aimed at shedding light on mechanisms and conditions controlling the performance of the LSWF. − Nonetheless, conventional experimental investigations imply that studies under laboratory conditions suffer from some issues including time and budget constraints, uncertainty in the quality of the data obtained and their reliability, the requirement of highly skilled personnel to design and conduct laboratory experiments, and some other common limitations of experimental research such as slight differences in standards and procedures. In recent years, owing to accumulation of a considerable amount of experimental data including coreflooding experiments in sandstone reservoirs, application of machine learning (ML) and artificial intelligence (AI) has attracted interest in petroleum engineering research ranging from EOR to flow assurance. − As an example, AI-based models are used to estimate distribution of unwanted shale barriers in the SAGD procedure or provoke the acquisition of real-time water saturation from readily available well log data .…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Connectionist Models for Performance Prediction of Low Salinity Waterflooding in Sandstone Reservoirs

et al. 2021

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Low salinity waterflooding (LSWF) and its variants also known as smart water or ion tuned water injection have emerged as promising enhanced oil recovery (EOR) methods. LSWF is a complex process controlled by several mechanisms and parameters involving oil, brine, and rock composition. The major mechanisms and processes controlling LSWF are still being debated in the literature. Thus, the establishment of an approach that relates these parameters to the final recovery factor (RFf) is vital. The main objective of this research work was to use a number of artificial intelligence models to develop robust predictive models based on experimental data and main parameters controlling the LSWF determined through sensitivity analysis and feature selection. The parameters include properties of oil, rock, injected brine, and connate water. Different operational parameters were considered to increase the model accuracy as well. After collecting the relevant data from 99 experimental studies reported in the literature, the database underwent a comprehensive and rigorous data preprocessing stage, which included removal of duplicates and low-variance features, missing value imputation, collinearity assessment, data characteristic assessment, outlier removal, feature selection, data splitting (80–20 rule was applied), and data scaling. Then, a number of methods such as linear regression (LR), multilayer perceptron (MLP), support vector machine (SVM), and committee machine intelligent system (CMIS) were used to link 1316 data samples assembled in this research work. Based on the obtained results, the CMIS model was proven to produce superior results compared to its counterparts such that the root mean squared rrror (RMSE) values for both training and testing data are 4.622 and 7.757, respectively. Based on the feature importance results, the presence of Ca2+ in the connate water, Na+ in the injected brine, core porosity, and total acid number of the crude oil are detected as the parameters with the highest impact on the RFf. The CMIS model proposed here can be applied with a high degree of confidence to predict the performance of LSWF in sandstone reservoirs. The database assembled for the purpose of this research work is so far the largest and most comprehensive of its kind, and it can be used to further delineate mechanisms behind LSWF and optimization of this EOR process in sandstone reservoirs.

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Effects of cation salinity on the dynamic interfacial tension and viscoelasticity of a water-oil system

Cited by 27 publications

References 53 publications

Influence of CTAB-Grafted Faujasite Nanoparticles on the Dynamic Interfacial Tension of Oil/Water Systems

Influence of CTAB-Grafted Faujasite Nanoparticles on the Dynamic Interfacial Tension of Oil/Water Systems

Effects of Oil and Sediment Properties and Mixing States on the Formation and Settling of Oil‐Particle Aggregates

Data-Driven Connectionist Models for Performance Prediction of Low Salinity Waterflooding in Sandstone Reservoirs

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