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Review

Micro/Nanoparticle Characteristics and Flow in Porous Media: A Review towards Enhanced Oil Recovery

1
School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China
2
National & Local Joint Engineering Lab for Big Data Analysis and Computing Technology, Beijing 100190, China
3
Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu 610218, China
4
School of Chemical Engineering, Qingdao University of Science and Technology, Qingdao 266042, China
*
Author to whom correspondence should be addressed.
Energies 2024, 17(16), 4136; https://doi.org/10.3390/en17164136
Submission received: 1 July 2024 / Revised: 10 August 2024 / Accepted: 14 August 2024 / Published: 20 August 2024
(This article belongs to the Section H: Geo-Energy)

Abstract

:
Micro/nanoparticles have emerged as pivotal agents in enhancing oil recovery (EOR), offering novel approaches to optimize the extraction processes in complex reservoirs. This review comprehensively examines the utilization of these particles, focusing on their unique material and structural characteristics that facilitate significant modifications in flow dynamics within porous media. These particles effectively reduce interfacial tension, modify wettability, and improve sweep efficiency, thereby enhancing oil recovery efficacy. Through a synthesis of current research spanning field-scale experiments, core flood studies, and micro-model investigations, this paper highlights the integration of micro/nanoparticles in practical EOR applications. Despite their proven potential, challenges such as scalability, environmental concerns, and economic feasibility persist, requiring ongoing advancements in particle engineering and simulation technologies. This review aims to provide a thorough understanding of the current landscape and future prospects of micro/nanoparticles in EOR, underlining the need for innovation and interdisciplinary collaboration to overcome existing hurdles and fully exploit these technologies in the oil and gas industry.

1. Introduction

Micro- and nanoparticles have emerged as a significant area of research in the field of enhanced oil recovery (EOR) due to their unique materials and structural attributes. These particles are typically engineered from a variety of materials including silica [1,2,3,4], polymers [5,6,7], and metals [8,9,10], each selected for specific surface properties and reactivity. Structurally, these particles can range from spherical forms to more complex shapes like rods or sheets [11,12,13], which can be further functionalized with various surface treatments to enhance their performance in reservoir environments. The precise engineering of these particles allows for targeted modifications that improve their stability, reactivity, and interaction with reservoir fluids and rocks [14,15,16,17,18].
The flow dynamics of micro/nanoparticles in porous media play a crucial role in their effectiveness for EOR applications [19]. These particles are specifically designed to manipulate physical and chemical properties at the microscopic level, effectively reducing interfacial tension and altering the wettability of reservoir rocks [20,21,22,23]. The reduction of interfacial tension helps in mobilizing trapped oil, while the alteration in wettability from oil-wet to water-wet enhances the displacement of oil by aqueous flooding methods [24,25,26]. These capabilities are critical as they directly influence the fluid dynamics within the porous structures of reservoir rocks, leading to more efficient oil recovery.
In the realm of enhanced oil recovery, micro/nanoparticles are applied in various innovative ways. These include serving as carriers for EOR chemicals, stabilizing emulsions to improve sweep efficiency, and even acting as catalysts to facilitate chemical reactions within the reservoir [13,27,28,29,30]. Their small size and large surface area make them ideal for navigating the complex pore spaces of reservoir rocks, where they can exert significant effects even at low concentrations. This makes them particularly valuable for improving the recovery of oil from mature or complex reservoirs where traditional methods have become less effective.
Current research on micro/nanoparticles spans various scales, including field-scale experiments and simulations [31,32,33,34,35], core flood studies [36,37,38,39], and micro-model investigations at the pore level [22,40]. These studies aim to bridge the gap between laboratory-scale insights and real-world applications, allowing researchers to optimize particle design and injection strategies based on empirical data and predictive modeling. By examining the behavior of these particles across different scales, scientists can better understand their interactions in realistic reservoir conditions, leading to more precise and scalable EOR solutions [41,42].
Future advancements in EOR technologies are requested to focus on the development of eco-friendly, biodegradable nanoparticles that can dynamically respond to changing reservoir conditions like pH, temperature, and salinity to optimize recovery processes in real time. To bridge the gap between lab-scale results and field applications, integrated and scalable methods combining chemical, thermal, and microbial recovery with nano-enhanced techniques should be developed, alongside continuous monitoring systems that adapt injection strategies based on real-time data. Furthermore, the incorporation of AI and advanced modeling, which involves machine learning and predictive analytics, can expedite the development of nanoparticles and enhance the precision of EOR processes. Coupled with cutting-edge imaging and sensing technologies, these innovations promise more accurate, efficient, and sustainable EOR solutions, revolutionizing oil recovery by aligning material science with technological advancements.
The purpose of this review paper is to consolidate the current understanding and recent advancements in the use of micro/nanoparticles in enhanced oil recovery. By examining the materials, structures, and dynamic behaviors of these particles, as well as their practical applications across various scales of EOR operations, this review aims to highlight the potential of micro/nanoparticles as a transformative technology in the oil and gas industry. The goal is to provide a comprehensive overview that not only elucidates the mechanisms behind their effectiveness but also identifies ongoing challenges and opportunities for future research and application in the field. The review structure is exhibited in Figure 1.

2. Materials and Structure Designs

2.1. Materials of Micro/Nanoparticles

Micro/nanofluids exhibit exceptional potential in improving oil recovery, especially in low permeability reservoirs. Their ability to significantly reduce oil/water interfacial tension and modify wetting properties has been confirmed through extensive studies [26,58,59,60]. For instance, a specific nanofluid was shown to decrease the oil/water interfacial tension by two orders of magnitude and increase the expansion modulus of the oil/water interface by 77% at equilibrium, facilitating the mobilization and migration of remaining oil in micro-to-nano-scale pores and throats, thus enhancing oil recovery efficiency [61]. Table 1 is a summary of the materials, size, structural designs, fabrication techniques, EOR mechanisms, and EOR performances of micro/nanoparticles. Figure 2 illustrates recent research on materials and structural designs of micro/nanoparticles and their mechanisms in EOR.
Recent studies have shown that aqueous dispersions of cellulose nanocrystals (CNCs) can undergo a substantial viscosity increase upon heat aging, especially at temperatures above 90 degrees Celsius [39,62]. This characteristic makes CNCs particularly useful in the EOR as the increased viscosity can improve macroscopic sweep efficiencies and mitigate viscous fingering [63]. The application of CNCs in a high-temperature core flood experiment demonstrated a significant oil recovery rate of 62.2%, where the mechanism of local log jams in the porous media played a crucial role [39].
Metal oxide nanoparticles, including variants such as iron oxide and aluminum oxide, are utilized in EOR for their ability to alter rock wettability and reduce interfacial tension between oil and water phases [22,64,65,66,67]. By modifying the wettability of reservoir rocks from oil-wet to water-wet, these nanoparticles can enhance the effectiveness of water flooding processes [67,68]. Additionally, their role in reducing interfacial tension facilitates the easier displacement of oil by water, leading to improved oil recovery rates [38,68]. The use of metal oxide nanoparticles is especially advantageous in reservoirs where traditional methods have been less effective.
Carbonate nanoparticles are particularly effective in environments with high brine content, common in many carbonate reservoirs. These nanoparticles work by sequestering ions in the brine, which can lead to significant changes in the chemistry of rock surfaces. This alteration can improve oil recovery by enhancing water flood performance and reducing residual oil saturation. The unique chemical interactions promoted by carbonate nanoparticles make them valuable for targeting the specific challenges associated with carbonate reservoirs, where oil recovery can be notoriously difficult [26,64].
Chitosan nanoparticles, derived from the natural biopolymer chitin, are noteworthy for their biodegradability and ability to stabilize emulsions [5]. These properties make them particularly useful in EOR applications where environmental concerns are paramount. Chitosan nanoparticles can improve the mobilization of oil, particularly in formations where oil is trapped in fine pores. By stabilizing the emulsion, these nanoparticles facilitate the movement of oil towards production wells, enhancing overall recovery while maintaining an eco-friendly profile.
Functionalized SiO2 nanoparticles have shown promise in EOR through their ability to modify interfacial properties and stabilize emulsions. The integration of sodium oleate on nano-SiO2 particles has demonstrated enhanced stability and effectiveness in oil recovery, attributed to the amphiphilic nature of the modified nanoparticles. These nanoparticles assist in reducing the interfacial tension and improving the wettability of rock surfaces, thus facilitating more effective oil displacement [2,3,69,70,71,72].
PSiNPs have been explored for their potential in EOR through core- and micro-scale studies [4]. These nanoparticles can significantly influence oil displacement efficiency by altering wettability conditions within the reservoir. Experiments have indicated that different wettability conditions (water-wet, intermediate-wet, and oil-wet) can affect the flow behavior and ultimately the oil recovery efficiency in porous media [73].
These materials highlight the diverse and effective roles that micro/nanoparticles can play in enhancing oil recovery. Their unique properties allow for the manipulation of flow dynamics and interfacial tensions within porous media, which are critical factors in optimizing oil recovery processes.
Figure 2. Materials and structural designs of micro/nanoparticles. (a) Spherical P (D-PGMA-EGDMA) particles [44], (b) wrinkled P (D-PGMA-DVB) particles [44], (c) carbon nanotubes [45], (d) nanoporous graphene [74], (e,f) Mg-Al layered double hydroxides sheet [47].
Figure 2. Materials and structural designs of micro/nanoparticles. (a) Spherical P (D-PGMA-EGDMA) particles [44], (b) wrinkled P (D-PGMA-DVB) particles [44], (c) carbon nanotubes [45], (d) nanoporous graphene [74], (e,f) Mg-Al layered double hydroxides sheet [47].
Energies 17 04136 g002

2.2. Fabrications and Designs of Micro/Nanoparticles

The fabrication methods employed to produce micro/nanoparticles are pivotal in determining their functional characteristics and suitability for the EOR applications. Chemical vapor deposition (CVD) [75], sol–gel processes [11,76,77], and controlled precipitation [78] are common techniques. These methods are particularly useful for synthesizing nanoparticles like SiO2 or metal oxides with specific surface chemistries and functionalities. For example, nanoemulsions and hybrid nanostructures can be synthesized using sol–gel processes that allow for the encapsulation of surfactants or polymers, thereby enhancing their stability and functional performance in EOR applications.
Self-assembly is a powerful technique for forming organized, functional structures without external direction. This method is often utilized to create layered or core-shell nanostructures, which are pivotal in developing nanoemulsions and porous nano/microspheres. Self-assembling surfactant molecules can form micelles or vesicles, which are fundamental to nanoemulsion creation. Additionally, self-assembly can be directed by environmental stimuli such as pH, temperature, and ionic strength, making it ideal for creating responsive materials that can adapt to different conditions in the reservoir [12,66,79].
Template-based methods involve using a pre-formed template to shape the structure of the desired material. Templates can be biological, like virus particles or DNA structures, or synthetic, such as nanoporous alumina molds. This method is particularly relevant for creating porous nano/microspheres with specific pore sizes and surface properties. The controlled release mechanisms of these spheres can be finely tuned by adjusting the characteristics of the template [1,8,80]. Electrospinning is used to create fibrous nanostructures from polymer solutions or melts. This technique can produce fibers with diameters in the nanometer to micrometer range, useful for creating mats or weaves that can serve as oil-absorbent materials or filters in EOR processes [73,81,82]. The high surface area-to-volume ratio of electrospun fibers makes them ideal for applications requiring rapid interactions with oil or other reservoir fluids.
The structural design of micro/nanoparticles is the key part in fabrication and crucial in optimizing their functionality and effectiveness in EOR processes. Nanoemulsions are a structural design that has shown great promise in EOR applications. These are colloidal dispersions of nano-sized droplets of one liquid in another, typically oil in water or vice versa, stabilized by surfactants. The ultra-small droplet size and the increased surface area significantly enhance the ability of the fluid to reduce interfacial tension and improve oil mobility. The structure of these emulsions facilitates the modification of reservoir rock wettability, improving oil displacement and recovery [83,84,85].
The development of hybrid nanostructures, which combine different materials or functionalities within a single particle, has opened new pathways for designing more effective EOR agents [13,84,85]. For example, silica nanoparticles coated with responsive polymers can adapt their behavior based on environmental conditions such as pH, temperature, or salinity [4]. These smart materials can switch between hydrophilic and hydrophobic states, allowing for dynamic interaction with the reservoir environment to enhance oil recovery. The incorporation of magnetic nanoparticles within these structures also allows for the directed movement and positioning within the reservoir using external magnetic fields, providing a method to target specific zones within the reservoir for treatment.
Porous nano/microspheres offer a controlled release mechanism for EOR chemicals, such as surfactants or polymers. These structures can be engineered to release their contents slowly over time or in response to specific environmental triggers in the reservoir, such as changes in temperature or pressure. This controlled release ensures a sustained action of the EOR chemicals, improving efficiency and reducing the amount of chemicals required. Additionally, the porosity and surface area of these particles are designed to maximize interaction with oil and water phases, which enhances emulsion stability and displacement efficiency [1,20,86].
Recent advancements have also explored the use of shape memory alloys (SMAs) at the nano-scale in particle design for EOR. SMAs can change their shape in response to temperature changes, which can be used to open or close microscopic channels in the reservoir or to release EOR chemicals at specific reservoir conditions. This dynamic structural change can be used to enhance the selective permeability and fluid flow within the reservoir, offering a novel approach to managing reservoir properties and enhancing oil recovery [87,88].
These structured designs of micro/nanoparticles are integral to enhancing the delivery and function of enhanced oil recovery (EOR) agents. By capitalizing on the distinctive characteristics of these designs, researchers and engineers are able to notably augment the efficiency and effectiveness of oil recovery methods. This approach allows for customization and optimization tailored to the unique geological and chemical properties of specific reservoirs. Consequently, this leads to more strategic deployment of resources, minimizes environmental impact, and improves the overall economic viability of recovery operations, aligning with the industry’s shift towards more sustainable and precision-driven extraction techniques.

3. EOR Mechanisms of Micro/Nanoparticles

3.1. Reduction of Interfacial Tension

The reduction of interfacial tension (IFT) between oil and water phases is a critical mechanism by which micro/nanoparticles enhance oil recovery. The ability of particles to lower the IFT facilitates the easier displacement of oil by the injection fluids, which is a key factor in improving oil recovery efficiency. This section explores the role of various nano-engineered particles in reducing IFT and highlights the advancements and results documented in experimental studies. Figure 3 depicts the EOR mechanisms of these micro/nanoparticles.
Micro/nanoparticles are particularly effective due to their ability to adsorb at the oil–water interface [18,60,92,93]. This is largely attributed to their amphiphilic surface properties, which enable them to interact favorably with both oil and water molecules. By accumulating at the interface, these particles reduce the contact area between oil and water, effectively lowering the interfacial tension. The particles modify the surface energies of the oil and water phases, which is a direct measure of the energy required to increase the interface area between them. When particles occupy this interface, they alter these energies by creating a barrier that diminishes the molecular interactions across the interface. Beyond just reducing interfacial tension, micro/nanoparticles play a crucial role in stabilizing emulsions—mixtures of oil and water where one phase is dispersed in the other in the form of droplets [18,30,61,68]. These particles help stabilize the emulsions by coating the droplets and preventing them from coalescing. This stabilization is critical in EOR as it aids in the mobilization of trapped oil, enhancing its flow towards production wells, thereby maintaining lower interfacial tension over extended periods.
The shape, size, and specific surface modifications of particles can significantly influence their effectiveness in reducing interfacial tension. For instance, Janus particles, which have distinct surface properties on different sides, can orient themselves at the interface in a way that optimally reduces interfacial tension [94,95]. Additionally, particles can exert mechanical forces at the interface, such as capillary forces or by forming a rigid film that resists deformation [96,97]. This added mechanical stability can help minimize fluctuations at the interface, contributing to a lower and more stable interfacial tension. The interactions among the particles themselves, like forming networks or chains, also play a role in stabilizing the interface.
The combination of nanoparticles with surfactants can lead to synergistic effects in reducing IFT [30]. This combination often results in a more significant reduction of IFT compared to the use of nanoparticles or surfactants alone. The nanoparticles can act as a carrier for the surfactants, ensuring a more uniform distribution across the reservoir and at the oil–water interface. Additionally, the solid nature of nanoparticles adds a mechanical stability to the interface, which aids in the more effective displacement of oil.
The reduction of interfacial tension by micro/nanoparticles plays a pivotal role in enhancing oil recovery. Through various mechanisms, such as adsorption at the interface and modification of surface properties, these particles significantly lower the energy barrier for oil displacement. Advanced experimental setups and synergistic formulations further highlight the potential of micro/nanoparticles in revolutionizing enhanced oil recovery strategies, making them indispensable in the toolkit for improving oil recovery in challenging reservoirs.

3.2. Alteration of Wettability

Alteration of wettability is a crucial mechanism through which micro/nanoparticles enhance oil recovery by modifying the interaction between the rock surface and the fluids within the reservoir. Wettability alteration refers to the change from an oil-wet to a water-wet state (or vice versa), which significantly impacts the efficiency of oil displacement processes. This section focuses on how various micro/nanoparticles achieve wettability alteration and the resulting benefits in EOR applications.
Micro/nanoparticles can alter the wettability of reservoir rocks through several mechanisms. The primary method involves the adsorption of particles onto the rock surfaces, which modifies the surface chemistry and, consequently, its interaction with oil and water. For instance, silica nanoparticles have been shown to deposit on rock surfaces, changing their wettability from oil-wet to water-wet [3]. This change enhances the rock’s affinity for water, promoting more effective water flooding and displacing more oil towards the production wells.
The functionalization of nanoparticles plays a vital role in their ability to alter wettability. By attaching specific functional groups to the surfaces of nanoparticles, researchers can target and control the interaction between the nanoparticles and the rock surfaces. For example, nanoparticles coated with surfactants or polymers that have hydrophilic groups can increase the hydrophilicity of rock surfaces, effectively altering their wettability [4]. This tailored surface chemistry is crucial for achieving the desired fluid dynamics in the reservoir, facilitating improved sweep efficiency and oil recovery.
Experimental studies using core flooding and microfluidic devices have provided substantial evidence of the efficacy of nanoparticles in wettability alteration. In these experiments, treated rock samples exhibit changes in their wetting properties, as observed through contact angle measurements [36,37,39]. The results show a decrease in the contact angle, indicating a shift towards more water-wet conditions. Furthermore, nanoemulsions and microemulsions containing nanoparticles have been utilized to enhance this effect, demonstrating significant improvements in oil recovery rates in laboratory settings [98].
The alteration of wettability by nanoparticles is often used in conjunction with other EOR techniques, such as polymer flooding and surfactant flooding. Nanoparticles can synergize with these methods by stabilizing the injected fluids and ensuring that the altered wettability is maintained throughout the flooding process. This synergy prevents the reversion of wettability and supports a more uniform displacement front, which is critical for maximizing oil recovery.
The alteration of wettability has a direct impact on the effectiveness of oil recovery operations. By shifting the wettability towards more water-wet conditions, nanoparticles increase the likelihood of oil displacement by aqueous phases. This change facilitates the mobilization of trapped oil droplets, reducing residual oil saturation and enhancing overall recovery efficiency. The strategic use of nanoparticles for wettability alteration thus represents a promising approach to overcome the challenges associated with heterogeneous and oil-wet reservoirs.

3.3. Improvement of Sweep Efficiency

Sweep efficiency is a critical measure in the EOR that indicates the extent to which injected fluids, such as water or gas, effectively displace oil within the reservoir. Improving sweep efficiency is essential for maximizing oil recovery, minimizing the bypassing of oil, and enhancing the overall economic viability of oil production operations. Micro- and nanoparticles play a pivotal role in this context by modifying the reservoir properties and the dynamics of fluid flow.
Micro/nanoparticles contribute to improved sweep efficiency through several mechanisms. One primary method is by reducing the permeability of high-permeability zones, or “thief zones”, which tend to allow the rapid passage of injected fluids, leaving lower permeability zones under-swept [6,89,99]. By plugging these high-permeability pathways, particles ensure a more uniform fluid front and promote the diversion of fluids into unswept areas. Silica nanoparticles, for example, can accumulate at pore throats and selectively reduce permeability, thus equalizing the flow paths within the reservoir.
As discussed in previous sections, the alteration of rock wettability from oil-wet to water-wet conditions by micro/nanoparticles also contributes to enhanced sweep efficiency. Water-wet conditions improve the displacement efficiency by allowing water-based EOR fluids to spread more uniformly across the reservoir rock surfaces, facilitating a more effective displacement of oil. This change in wettability, coupled with the strategic use of nano-sized carriers for surfactants or polymers, ensures that the modifications in rock properties are both effective and long-lasting.
Micro/nanoparticles improve macroscopic sweep efficiency by affecting the overall fluid dynamics within the reservoir. By ensuring a more homogeneous fluid distribution and reducing channeling effects, these particles support a more efficient oil recovery process. For instance, polymer-coated silica nanoparticles have been shown to enhance the macroscopic homogeneity of polymer floods, leading to a more uniform displacement front and reduced residual oil saturation [4,30,100].
Experimental studies and field trials have provided substantial evidence supporting the effectiveness of micro/nanoparticles in improving sweep efficiency [101]. Laboratory tests often employ micro-models and core flooding setups to visualize and quantify the impact of these particles on fluid distribution and oil recovery. Field applications, although more complex, have demonstrated that nanoparticles can be successfully scaled from the laboratory to the field, showing a significant improvement in oil recovery rates and economic returns.
The use of micro/nanoparticles represents a promising strategy to enhance sweep efficiency in EOR processes. Through mechanisms such as permeability modification, viscosity enhancement, and wettability alteration, these particles optimize the distribution and efficacy of injected fluids across the reservoir. Continued advancements in nanoparticle technology and its integration into EOR strategies are expected to play a crucial role in the future of efficient and sustainable oil recovery.

3.4. Interfacial Chemical Reactions

Interfacial chemical reactions facilitated by micro/nanoparticles are crucial in the EOR processes, influencing both the efficiency and effectiveness of oil extraction from reservoirs. These reactions occur at the interface between the oil and water phases and are primarily responsible for altering the chemical and physical properties of the reservoir fluids and rock surfaces. The introduction of micro/nanoparticles into EOR techniques helps catalyze and control these reactions, leading to improved oil recovery.
Nanoparticles such as nano-sized metals or metal oxides act as catalysts in chemical reactions at the oil–water interface [2]. For example, nanoparticles can catalyze the breakdown of heavy hydrocarbons into lighter, more mobile forms, facilitating their flow towards production wells [102,103]. Additionally, metallic nanoparticles like iron oxides or zinc oxides can engage in redox reactions with reservoir fluids or rock formations, altering the chemical environment within the reservoir to favor oil recovery [8,104].
Micro/nanoparticles also play a significant role in enhancing polymer flooding processes by participating in crosslinking reactions that increase the viscosity of the flooding medium [105]. This increased viscosity improves sweep efficiency, as previously discussed, but also relies on intricate interfacial chemical reactions that enhance the stability and longevity of the polymer in the harsh reservoir environment. Nanoparticles can be engineered to react with specific components of the polymer or with the reservoir rock to optimize the performance of the flooding process.
Innovative applications of micro/nanoparticles in EOR also include their use in facilitating biochemical reactions [102,106]. Biodegradable nanoparticles can deliver enzymes or other biochemical agents that react with oil or sediments at the interface. These reactions can lead to the biodegradation of heavy oils into lighter components or the removal of scaling from rock surfaces, which enhances oil flow and recovery.
Interfacial chemical reactions induced by micro/nanoparticles significantly enhance the efficiency of EOR processes. By catalyzing reactions that reduce interfacial tension, alter wettability, and increase the mobility of oil, these particles play a pivotal role in optimizing oil recovery. Future advancements in nanoparticle technology and a deeper understanding of interfacial chemistry will continue to drive innovations in EOR strategies, offering more effective solutions for oil recovery from complex reservoirs.

4. Experimental Findings and Computational Modeling

4.1. Field Scale Experiments and Simulations

Field scale experiments and simulations are critical in the evaluation and optimization of the EOR techniques. These large-scale studies bridge the gap between laboratory research and practical application, providing insights into the performance and feasibility of various EOR strategies under real reservoir conditions. The application of micro/nanoparticles in field-scale EOR has shown promising results, demonstrating significant improvements in oil recovery efficiency. This review focuses on the findings from field-scale experiments and simulations involving micro/nanoparticles, highlighting their impact on EOR processes. Table 2 provides a summary of research scales, methods, schemes, goals, advantages, and limitations of micro/nanoparticle-based EOR.
Field scale experiments involve the deployment of EOR techniques in actual oil reservoirs, allowing researchers to observe the interactions between micro/nanoparticles and reservoir fluids and rocks under true operating conditions. One of the primary goals of these experiments is to assess the scalability and practical effectiveness of laboratory-proven methods [107]. In these experiments, parameters such as injection rates, fluid compositions, and particle concentrations are optimized to maximize oil recovery.
Several field trials have demonstrated the effectiveness of nanoparticle-enhanced EOR techniques. For instance, the injection of silica nanoparticles into a mature oil field resulted in a notable increase in oil production. A field pilot test for the Sarukawa oil field in Japan using silica nanoparticles reported over 10% EOR in both a short-term injection test and a long-term inter-well test [108]. A field trial application performed by silica nanofluids in a Colombina oil field demonstrated an EOR of 27.3%, way higher than the expectation of 3.5% [109]. The nanoparticles improved the sweep efficiency by plugging high-permeability zones and promoting a more uniform displacement front. Additionally, these particles altered the wettability of the reservoir rock, making it more water-wet, and thereby enhancing water flooding efficiency. In addition, oil fields in the Middle East were subjected to a nanoparticle-based EOR technique, resulting in a significant increase in oil production. A two-day field injection of carbon nanofluids into the Ghawar field in Saudi Arabia has shown a dramatic cumulative oil recovery of 86% [110,111].
Computational modeling and simulations play a crucial role in understanding and predicting the behavior of micro/nanoparticles in EOR applications at the field scale [112,113]. These simulations align with and help in designing experiments, optimizing injection strategies, and scaling up laboratory findings to field conditions. Advanced simulation tools such as computational fluid dynamics (CFD) and reservoir simulation software are used to model the complex interactions between nanoparticles, fluids, and reservoir rocks.
Simulations of nanoparticle-enhanced EOR often incorporate detailed models of fluid flow, particle transport, and interfacial phenomena. For example, simulations have shown that nanoparticles can effectively reduce interfacial tension and alter wettability, leading to improved oil displacement [89,114,115]. These models also help in identifying the optimal particle size, concentration, and injection timing to maximize recovery while minimizing operational costs.
While field scale experiments and simulations have shown promising results, several challenges remain. The deployment of nanoparticles in field conditions requires careful consideration of factors such as particle stability, compatibility with reservoir fluids, and potential environmental impacts. Moreover, the cost of nanoparticle production and injection can be a limiting factor for large-scale applications. Future research should focus on developing more cost-effective and environmentally friendly nanoparticles, as well as improving the understanding of nanoparticle behavior under varying reservoir conditions. Enhanced computational models that incorporate real-time data from field trials can also aid in optimizing EOR strategies and ensuring their successful implementation.

4.2. Core Flood Experiments and Simulations

Core flood experiments are fundamental to understanding the mechanisms of EOR at a more detailed scale. These experiments involve the injection of EOR agents, such as micro/nanoparticles, into core samples extracted from reservoirs. By simulating reservoir conditions in the laboratory, core flood experiments provide valuable data on fluid dynamics, particle behavior, and the interactions between injected agents and reservoir rocks. This review discusses the findings from core flood experiments and the role of simulations in optimizing these processes.
Core flood experiments are designed to replicate the conditions within a reservoir as closely as possible. Core samples, typically cylindrical pieces of rock, are saturated with reservoir fluids (oil and water) and placed within a core holder. EOR agents, such as nanoparticle suspensions, are then injected into the core under controlled conditions of pressure and temperature. The experimental setup allows researchers to measure parameters such as fluid displacement, pressure drop, and oil recovery efficiency.
In these experiments, silica nanoparticles, for instance, have been shown to significantly enhance oil recovery by altering the wettability of the core samples and reducing interfacial tension. The nanoparticles can also improve sweep efficiency by plugging high-permeability pathways and promoting a more uniform displacement front. A functionalized SiO2 nanofluid was developed and tested in a core sampled from carbonate reservoirs, indicating a 20.8% EOR [36]. In addition to EOR rate, the depressurization is also a significant factor to evaluate the nanoparticle performances. The nano-scale SiO2-fluorinated acrylate polymer nanoemulsions (SCFs) have demonstrated a 29.62% depressurization on the injection during the core flooding of a sample from a low-permeability reservoir [37]. A new synthetic nano-composite called UiO-66-NH2/TiO2 (UT) was proposed and according to the core flooding in a carbonate rock sample, a 26% EOR was observed [38]. Core flood experiments help in understanding these mechanisms and in quantifying the improvements in oil recovery.
Results from core flood experiments have demonstrated the effectiveness of various nanoparticles in enhancing oil recovery. For example, experiments with silica nanoparticles have shown a reduction in residual oil saturation by up to 30%, indicating a significant improvement in oil displacement efficiency [70]. Similarly, experiments with polymer-coated nanoparticles have revealed enhanced stability and performance under reservoir conditions, leading to higher oil recovery rates. Nevertheless, the EOR rate is not the only parameter we should focus on. The aqueous cellulose nanocrystal (CNC) dispersions are developed as thickening and clogging agents upon heat aging in the reservoirs. Even though the EOR rate reported from core floodings is only 1.2%, the CNC remarkably shift the core to be homogeneous, which facilitates the next cycle of flooding and EOR [39]. The core flood experiments have also highlighted the importance of particle size, concentration, and surface functionality. Smaller nanoparticles with tailored surface properties can penetrate deeper into the core, interact more effectively with the rock surfaces, and provide better results in terms of oil recovery. The optimization of these parameters is crucial for maximizing the benefits of nanoparticle-enhanced EOR.
Computational simulations play a vital role in complementing core flood experiments. These simulations use mathematical models to replicate the physical processes observed in the laboratory, allowing for a more comprehensive analysis of fluid dynamics and particle behavior. Advanced simulation tools, such as computational fluid dynamics (CFD) and reservoir simulation software, are used to model the injection and transport of nanoparticles within the core samples. Simulations help in understanding the complex interactions between nanoparticles, fluids, and rock surfaces. For instance, models can predict the adsorption of nanoparticles on rock surfaces, the reduction in interfacial tension, and the changes in wettability. These predictions are validated against experimental data, ensuring the accuracy and reliability of the models. Laboratory core flooding experiments were simulated using CMG-STARS and with the presence of silica nanoparticles, an 18.49% EOR was obtained [116]. Simulations at lab-scale were made to validate the nanoparticles retention and remobilization during oil recovery, and results suggested the EOR rates of nanoparticle flooding are 6% higher than polymer flooding and 15% better than water flooding [115]. Simulations also aid in optimizing the experimental conditions, such as injection rates and particle concentrations, to achieve the best possible results.
Several case studies have illustrated the successful application of core flood simulations in optimizing EOR strategies. In one case, silica nanoparticles were tested in core flood simulations and the results were leveraged to guide the enhance oil recovery experiments from sandstone samples [117,118]. The experiments showed a significant increase in oil displacement, which was supported by simulation results predicting improved sweep efficiency and reduced residual oil saturation. In another study, polymer-coated nanoparticles were tested in carbonate cores. The core flood simulations demonstrated the nanoparticles’ ability to alter wettability and improve oil recovery, while simulations provided insights into the optimal particle size and concentration for maximum effectiveness. These studies highlight the importance of integrating experimental and simulation approaches to develop efficient EOR methods.
Despite the promising results, several challenges remain in the application of core flood experiments and simulations. One major challenge is ensuring the stability and compatibility of nanoparticles under reservoir conditions. Additionally, the scale-up of laboratory findings to field applications requires careful consideration of factors such as reservoir heterogeneity and economic feasibility. Future research should focus on developing more robust and versatile nanoparticles that can withstand the harsh conditions of reservoirs. Improved simulation models that incorporate real-time data from core flood experiments can enhance the predictive capabilities and reliability of these studies. Continued collaboration between experimentalists and modelers will be crucial for advancing the field of nanoparticle-enhanced EOR.

4.3. Micro-Model Experiments and Pore-Scale Simulations

Micro-model experiments are essential for visualizing and understanding fluid flow and transport phenomena at the pore scale. These experiments use transparent microfluidic devices or etched glass models that replicate the porous structure of reservoir rocks, allowing direct observation of the interactions between injected EOR agents, such as micro/nanoparticles, and the resident fluids. Direct visualization experiments showed that the recovery efficiency under the nano-micron polymer dispersion state could be enhanced by approximately 14.7% compared to that of the water flooding [7]. By providing detailed visual and quantitative data, micro-model experiments offer valuable insights into the mechanisms underlying enhanced oil recovery. Figure 4 exhibits the recent research progress on leveraging micro-model experiments to investigate micro/nanoparticles for EOR.
Micro-model experiments are conducted using devices that simulate the pore-scale structure of reservoir rocks. These devices are typically made from materials such as glass or silicon, with microchannels etched or molded to mimic the geometry and connectivity of pore spaces [122]. The micro-models are then saturated with reservoir fluids, and EOR agents are injected under controlled conditions. High-resolution imaging techniques, such as optical microscopy and fluorescence microscopy, are used to visualize fluid displacement and particle behavior in real time. In these experiments, researchers can observe how nanoparticles interact with oil and water phases, modify wettability, and reduce interfacial tension. For example, ZrO2 nanoparticles can be seen accumulating at the oil–water interface, forming stable emulsions that enhance oil displacement and resulting an above 10% EOR [123]. Micro-model experiments also allow the study of multiphase flow dynamics, providing insights into how nanoparticles influence the flow patterns and improve sweep efficiency.
Results from micro-model experiments have shown that nanoparticles can significantly enhance oil recovery by improving the efficiency of fluid displacement. For instance, experiments with TiO2 nanoparticles have demonstrated their ability to disperse remaining oil into smaller droplets, which are more easily mobilized by the injected fluids, and illustrated a surprising 51% improvement of heavy oil recovery in micro-model tests [124]. Additionally, these experiments have revealed the formation of stable emulsions that prevent the coalescence of oil droplets, further aiding in the displacement process.
Micro-model experiments have also highlighted the role of particle size and surface functionality in enhancing oil recovery. Smaller nanoparticles with hydrophilic or hydrophobic surface modifications can more effectively penetrate and interact with the pore spaces, leading to improved displacement efficiency. The real-time visualization provided by these experiments helps in understanding the dynamic behavior of nanoparticles and optimizing their design for specific reservoir conditions [122].
Pore-scale simulations complement micro-model experiments by providing a detailed numerical representation of fluid flow and particle transport within porous media. These simulations use computational models to replicate the geometry and properties of the pore spaces, allowing for a more comprehensive analysis of the interactions between EOR agents and reservoir fluids. Advanced simulation techniques, such as the lattice Boltzmann method (LBM) and computational fluid dynamics (CFD), are used to model the complex flow dynamics and interfacial phenomena at the pore scale. Pore-scale simulations also provide insights into the mechanisms by which nanoparticles enhance oil recovery. For example, simulations can predict how nanoparticles alter wettability and reduce interfacial tension, leading to improved fluid displacement. These models can also simulate the transport and distribution of nanoparticles within the pore spaces, helping to identify optimal injection strategies and particle concentrations [125,126,127].
Despite promising results, several challenges remain in the application of micro-model experiments and pore-scale simulations. One major challenge is accurately replicating the complex geometry and heterogeneity of natural reservoir rocks in micro-models and simulations. Additionally, the scalability of findings from pore-scale studies to field-scale applications requires careful consideration of factors such as reservoir heterogeneity and economic feasibility. Future research should focus on developing more realistic and scalable micro-models that better replicate the properties of reservoir rocks. Improved simulation techniques that incorporate real-time data from micro-model experiments can enhance the predictive capabilities and reliability of these studies. Continued collaboration between experimentalists and modelers will be crucial for advancing the field of nanoparticle-enhanced EOR.

5. Challenges and Limitations

The EOR technologies, particularly those involving micro/nanoparticles, have shown promising potential in laboratory settings and select field trials. However, several bottlenecks persist that limit their widespread adoption. The complex and heterogeneous nature of subsurface reservoirs poses significant challenges, as these geological variations can dramatically affect the distribution and efficacy of injected particles, making outcomes difficult to predict and generalize across different fields. Table 3 summarizes the recent bottleneck of EOR, future directions, and big pictures of the future advanced technology.
The economic feasibility of nano-enhanced EOR techniques is often questionable, particularly when considering the scale of investment required for wide-scale deployment. High production costs for specialized nanoparticles, coupled with the expenses of modification, deployment, and extensive post-injection monitoring, makes these technologies costly. To bridge the gap between lab-scale results and field applications, it is essential to develop more cost-effective and scalable production methods for nanoparticles.
A critical yet often under-discussed aspect of these technologies is their energy consumption. The synthesis, functionalization, and deployment of micro/nanoparticles for EOR purposes involve energy-intensive processes. For instance, the production of nanoparticles often requires high temperatures and pressures, while their functionalization may involve additional chemical processes, each consuming considerable amounts of energy. Moreover, the injection and distribution of these particles across vast reservoirs demand substantial energy inputs, which can negate some of the environmental benefits these technologies aim to provide. This energy footprint needs to be carefully evaluated against the incremental oil recovery gains to assess the true sustainability of nano-enhanced EOR solutions. Such assessments are crucial, especially in an era where the energy industry is under increasing pressure to reduce carbon emissions and enhance energy efficiency.
The current generation of micro/nanoparticles face significant limitations that stem from their material properties and design. Stability under reservoir conditions is a primary concern; nanoparticles can degrade or agglomerate in response to high temperatures, pressure variations, and salinity levels typical of many oil reservoirs. Such instability can diminish their effectiveness in modifying interfacial tensions or altering rock wettability as designed. Additionally, the reusability of these particles in multiple injection cycles remains problematic, which can further limit their cost-effectiveness. From an environmental standpoint, the long-term impact of nanoparticles, particularly their toxicity and persistence in ecosystems, is not fully understood. The potential for nanoparticles to contaminate groundwater or disrupt local biomes poses serious environmental risks, necessitating more comprehensive studies and stringent regulatory oversight before broader application.
Each primary research method used in developing and testing EOR technologies presents unique challenges. Experimental methods, while invaluable, often do not fully replicate the multifaceted conditions encountered in actual reservoirs. This discrepancy can lead to significant differences between laboratory and field performance, sometimes resulting in the overestimation of the technology’s efficacy. Numerical simulations, although useful for theoretical predictions and scaling analyses, require high computational resources and are often limited by the simplifications needed to make complex reservoir conditions computationally manageable. These models may overlook critical factors such as micro-scale fluid interactions or the detailed chemistry of particle-surface interactions. Furthermore, mathematical modeling of fluid dynamics in porous media is still developing, particularly in accurately modeling nano-scale phenomena. The complex interplay of fluid mechanics, particle dynamics, and reservoir characteristics involves non-linear behaviors that are difficult to predict and require sophisticated analytical techniques that are still under development.
To address these challenges, integrating AI and machine learning can offer significant advantages. AI-driven predictive analytics can enhance the design and synthesis of nanoparticles by modeling their interactions with reservoir fluids and rocks under various conditions, reducing the reliance on experimental trial-and-error approaches. Machine learning algorithms can analyze large datasets from laboratory and field experiments, identifying patterns and optimizing EOR strategies. Additionally, AI can facilitate the development of continuous monitoring and adaptive control systems that adjust injection strategies based on real-time data, improving the efficiency and sustainability of EOR processes.

6. Future Directions

Future advancements in the EOR technologies must prioritize the development of new materials that address the stability, environmental, and economic concerns associated with current nanoparticles. Research should focus on biodegradable and environmentally friendly nanoparticles that minimize ecological risks and are capable of degrading into non-toxic byproducts. Materials such as bio-based polymers or naturally occurring minerals could offer sustainable alternatives to synthetic nanoparticles. Additionally, the development of smart materials that can respond dynamically to changes in reservoir conditions—such as pH, temperature, and salinity—promises to enhance the adaptability and efficiency of EOR methods. These materials would adjust their behavior to optimize recovery processes in real time, potentially reducing the need for extensive re-injections and minimizing energy consumption.
To overcome the current limitations in experimental approaches and to bridge the gap between lab-scale results and field applications, there is a need for more integrated and scalable methods. Hybrid methods that combine the strengths of chemical, thermal, and microbial recovery processes with nano-enhanced techniques could offer more robust solutions. For instance, coupling nanoparticle technology with low-salinity water flooding could leverage synergistic effects to enhance oil recovery while mitigating environmental impacts. Furthermore, the development of continuous monitoring and adaptive control systems that can adjust injection strategies based on real-time data will be crucial. These systems could optimize nanoparticle distribution and concentration dynamically, improving recovery efficiency while reducing wastage and environmental risks.
Advanced modeling and simulation techniques must be developed to better predict the behavior of micro/nanoparticles in complex reservoir conditions. Enhancing computational models to more accurately simulate the nano-scale interactions and their macro-scale impacts on oil recovery is essential. This includes the development of more sophisticated pore-scale models and the integration of machine learning algorithms that can learn from a combination of lab results and field data to predict outcomes with higher accuracy. Machine learning techniques, such as deep learning and reinforcement learning, can be employed to analyze large datasets generated from laboratory experiments and field tests, identifying patterns and optimizing EOR strategies.
Additionally, AI-driven predictive analytics can facilitate the design and synthesis of new nanoparticles by modeling their interactions with reservoir fluids and rocks under various conditions. By leveraging AI, researchers can expedite the discovery of nanoparticles with desired properties, significantly reducing the time and cost associated with experimental trial-and-error approaches. Furthermore, AI can enhance real-time monitoring and control systems by processing data from advanced sensors and imaging technologies [56,128,129], enabling more precise adjustments to injection parameters and improving the overall efficiency of EOR processes.
The application of cutting-edge imaging and sensing technologies can provide deeper insights into the micro-level interactions between nanoparticles and reservoir matrices. Techniques such as real-time 3D imaging and nano-sensing could revolutionize how researchers observe and understand the fundamental mechanisms of EOR at the pore level. These advanced technologies, combined with AI and machine learning, can facilitate the development of more accurate and predictive models, ultimately leading to more effective and sustainable EOR solutions.
The future of enhanced oil recovery (EOR) is poised at the intersection of material science, artificial intelligence (AI), and advanced monitoring technologies. This convergence facilitates the development of intelligent and highly efficient nanoparticles specifically engineered to enhance oil recovery processes. By integrating these multidisciplinary approaches, researchers are able to create solutions that are not only more effective but also environmentally considerate. These advancements lead to nanoparticles that can adapt to varying reservoir conditions, optimize recovery rates, and reduce the ecological footprint of extraction activities. Furthermore, the integration of AI enables predictive analytics and real-time decision-making, streamlining operations and addressing both ecological and economic concerns more proactively. This holistic approach promises a transformative shift in the oil recovery industry, emphasizing sustainability and efficiency.

7. Conclusions

This review has highlighted the critical role of micro/nanoparticles in enhancing oil recovery through their unique ability to manipulate flow dynamics within porous media. By reducing interfacial tension, altering wettability, and enhancing sweep efficiency, these particles significantly improve oil recovery processes across various experimental scales, from field trials to pore-scale simulations.
Despite their potential, the geological complexities of subsurface reservoirs present significant challenges, affecting particle distribution and efficacy, thereby complicating outcome prediction and generalization. Economic and energy concerns also arise, with high costs and energy consumptions associated with nanoparticle production, modification, and deployment, necessitating more cost-effective and scalable methods. Material limitations, such as stability under reservoir conditions and potential environmental risks like groundwater contamination, further complicate their application.
Future advancements in particle design, computational modeling, and the development of advanced microfluidic devices are essential to address these challenges. Integrating real-time data and adaptive systems into EOR operations could further enhance the efficiency and responsiveness of recovery processes. As the field evolves, continued innovation and multidisciplinary collaboration are essential to fully harness the capabilities of micro/nanoparticles, optimizing EOR techniques for better efficiency and sustainability.

Author Contributions

J.L.: conceptualization, chapter organization, and writing—original draft. H.C.: investigation, figure organization, and writing—original draft. Y.W.: investigation and reference collection. H.S.: writing—review and editing, supervision, and resources. All authors have read and agreed to the published version of the manuscript.

Funding

The authors acknowledge the funding support from the National Natural Science Foundation of China (Grant No. 52274027, 52104028).

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References

  1. Guan, X.; Jiang, H.; Ngai, T. Pickering High Internal Phase Emulsions Templated Super-Hydrophobic-Oleophilic Elastic Foams for Highly Efficient Oil/Water Separation. ACS Appl. Polym. Mater. 2020, 2, 5664–5673. [Google Scholar] [CrossRef]
  2. Akay, G. Co-Assembled Supported Catalysts: Synthesis of Nano-Structured Supported Catalysts with Hierarchic Pores through Combined Flow and Radiation Induced Co-Assembled Nano-Reactors. Catalysts 2016, 6, 80. [Google Scholar] [CrossRef]
  3. Yao, C.; Zhang, X.; Li, L.; Lei, G. Investigation on flow resistance reduction and EOR mechanisms by activated silica nanofluids: Merging microfluidic experimental and CFD modeling approaches. J. Mol. Liq. 2022, 368, 120646. [Google Scholar]
  4. Omran, M.; Akarri, S.; Torsaeter, O. The Effect of Wettability and Flow Rate on Oil Displacement Using Polymer-Coated Silica Nanoparticles: A Microfluidic Study. Processes 2020, 8, 991. [Google Scholar] [CrossRef]
  5. Khare, P.; Yadav, A.; Ramkumar, J.; Verma, N. Microchannel-embedded metal-carbon-polymer nanocomposite as a novel support for chitosan for efficient removal of hexavalent chromium from water under dynamic conditions. Chem. Eng. J. 2016, 293, 44–54. [Google Scholar] [CrossRef]
  6. Wu, T.; Zhao, Y.; Zhang, Y.; Li, Z.; Su, J. Matching method between nanoparticle displacement agent size and pore throat in low permeability reservoir. Front. Chem. 2023, 11, 1289271. [Google Scholar] [CrossRef] [PubMed]
  7. Yue, M.; Zhu, W.; Han, H.; Song, H.; Long, Y.; Lou, Y. Experimental research on remaining oil distribution and recovery performances after nano-micron polymer particles injection by direct visualization. Fuel 2018, 212, 506–514. [Google Scholar] [CrossRef]
  8. Lumma, J.; Tjards, T.; Greve, E.; Saure, L.M.; Veziroglu, S.; Adelung, R.; Kienle, L.; Wolff, N.; Schütt, F. Synthesis of Highly Porous 3D Cerium Oxide Networks Designed for Catalytic Applications. Cryst. Growth Des. 2024, 24, 4914–4923. [Google Scholar] [CrossRef]
  9. Shokrlu, Y.H.; Babadagli, T. Viscosity reduction of heavy oil/bitumen using micro- and nano-metal particles during aqueous and non-aqueous thermal applications. J. Pet. Sci. Eng. 2014, 119, 210–220. [Google Scholar] [CrossRef]
  10. Goldstein, B.; Miloh, T. 3D controlled electrorotation of conducting tri-axial ellipsoidal nanoparticles. Phys. Fluids 2017, 29, 052008. [Google Scholar] [CrossRef]
  11. Sun, J.; Shi, X.; Du, Y.; Wu, Y. A robust, flexible superhydrophobic sheet fabricated by in situ growth of micro-nano-SiO2 particles from cured silicone rubber. J. Sol-Gel Sci. Technol. 2019, 91, 208–215. [Google Scholar] [CrossRef]
  12. Zhang, L.; Liu, Y.; Zeng, G.; Yang, Z.; Lin, Q.; Wang, Y.; Wang, X.; Pu, S. Two-dimensional Na-Bentonite@MXene composite membrane with switchable wettability for selective oil/water separation. Sep. Purif. Technol. 2023, 306, 122677. [Google Scholar] [CrossRef]
  13. Qu, F.; Cao, A.; Yang, Y.; Mahmud, S.; Su, P.; Yang, J.; He, Z.; Lai, Q.; Zhu, L.; Tu, Z.; et al. Hierarchically superhydrophilic poly(vinylidene fluoride) membrane with self-cleaning fabricated by surface mineralization for stable separation of oily wastewater. J. Membr. Sci. 2021, 640, 119864. [Google Scholar] [CrossRef]
  14. Wang, S.; Liu, K.; Han, J.; Ling, K.; Wang, H.; Jia, B. Investigation of Properties Alternation during Super-Critical CO2 Injection in Shale. Appl. Sci. 2019, 9, 1686. [Google Scholar] [CrossRef]
  15. Zhenping, L.; Ming, Q.; Jigang, Z.; Zhou, L.; Ping, S. Mechanism and Adaptability Evaluation of Well Soaking in Tight Reservoir. Chem. Technol. Fuels Oils 2022, 58, 181–184. [Google Scholar] [CrossRef]
  16. Zhu, W.; Li, B.; Liu, Y.; Song, H.; Wang, X. Solid-Liquid Interfacial Effects on Residual Oil Distribution Utilizing Three-Dimensional Micro Network Models. Energies 2017, 10, 2059. [Google Scholar] [CrossRef]
  17. Ding, B.; Xiong, C.; Geng, X.; Guan, B.; Pan, J.; Xu, J.; Dong, J.; Zhang, C. Characteristics and EOR mechanisms of nanofluids permeation flooding for tight oil. Pet. Explor. Dev. 2020, 47, 810–819. [Google Scholar] [CrossRef]
  18. Yang, K.; Wang, F.Y.; Zhao, J.Y. Experimental study of surfactant-enhanced spontaneous imbibition in fractured tight sandstone reservoirs: The effect of fracture distribution. Pet. Sci. 2023, 20, 370–381. [Google Scholar] [CrossRef]
  19. Song, H. Engineering Fluid Mechanics; Springer: Berlin/Heidelberg, Germany, 2018. [Google Scholar]
  20. Zhang, N.; Yang, N.; Zhang, L.; Jiang, B.; Sun, Y.; Ma, J.; Cheng, K.; Peng, F. Facile hydrophilic modification of PVDF membrane with Ag/EGCG decorated micro/nanostructural surface for efficient oil-in-water emulsion separation. Chem. Eng. J. 2020, 402, 126200. [Google Scholar] [CrossRef]
  21. Huo, X.; Sun, L.; Yang, Z.; Li, J.; Feng, C.; Zhang, Z.; Pan, X.; Du, M. Mechanism and Quantitative Characterization of Wettability on Shale Surfaces: An Experimental Study Based on Atomic Force Microscopy (AFM). Energies 2023, 16, 7527. [Google Scholar] [CrossRef]
  22. An, C.; Alfi, M.; Yan, B.; Killough, J.E. A new study of magnetic nanoparticle transport and quantifying magnetization analysis in fractured shale reservoir using numerical modeling. J. Nat. Gas Sci. Eng. 2016, 28, 502–521. [Google Scholar] [CrossRef]
  23. Lokanathan, M.; Wikramanayake, E.; Bahadur, V. Scalably manufactured textured surfaces for controlling wettability in oil-water systems. Mater. Res. Express 2019, 6, 046507. [Google Scholar] [CrossRef]
  24. Qin, G.W.; Liu, Y.; Liu, Q.; Sun, S.; Liu, F.; Qin, W.; Wang, X. Interfacial dilatational rheology and displacement mechanism of nano-flooding system. Can. J. Chem. Eng. 2024, 102, 713–721. [Google Scholar]
  25. Bacchin, P. An energy map model for colloid transport. Chem. Eng. Sci. 2017, 158, 208–215. [Google Scholar] [CrossRef]
  26. Zhang, B.; Mohamed, A.I.; Goual, L.; Piri, M. Pore-scale experimental investigation of oil recovery enhancement in oil-wet carbonates using carbonaceous nanofluids. Sci. Rep. 2020, 10, 17539. [Google Scholar] [CrossRef] [PubMed]
  27. Zhang, J.; Cheng, Y.; Yang, Q.; Liang, J.; Fang, Z.; Hou, X.; Chen, F. Research Progress of Femtosecond Laser Preparation of Durable Superhydrophohic Surface and Its Application. Acta Photonica Sin. 2022, 51. [Google Scholar] [CrossRef]
  28. Zhao, J.; Li, D.; Han, H.; Lin, J.; Yang, J.; Wang, Q.; Feng, X.; Yang, N.; Zhao, Y.; Chen, L. Hyperbranched Zwitterionic Polymer-Functionalized Underwater Superoleophobic Microfiltration Membranes for Oil-in-Water Emulsion Separation. Langmuir 2019, 35, 2630–2638. [Google Scholar] [CrossRef]
  29. Song, Y.; Song, Z.; Chen, Z.; Zhang, L.; Zhang, Y.; Feng, D.; Wu, Z.; Wu, J. Fluid phase behavior in multi-scale shale reservoirs with nano-confinement effect. Energy 2024, 289, 130027. [Google Scholar] [CrossRef]
  30. Pillai, P.; Saw, R.K.; Singh, R.; Padmanabhan, E.; Mandal, A. Effect of synthesized lysine-grafted silica nanoparticle on surfactant stabilized O/W emulsion stability: Application in enhanced oil recovery. J. Pet. Sci. Eng. 2019, 177, 861–871. [Google Scholar] [CrossRef]
  31. Kaneez, H.; Alebraheem, J.; Elmoasry, A.; Saif, R.S.; Nawaz, M. Numerical investigation on transport of momenta and energy in micropolar fluid suspended with dusty, mono and hybrid nano-structures. AIP Adv. 2020, 10, 045120. [Google Scholar] [CrossRef]
  32. Ok, S. Low-field NMR investigations on dynamics of crude oil confined into nanoporous silica rods and white powder. Front. Chem. 2023, 11, 1087474. [Google Scholar] [CrossRef] [PubMed]
  33. Luo, Y.; Zheng, T.; Xiao, H.; Liu, X.; Zhang, H.; Wu, Z.; Zhao, X.; Xia, D. Identification of distinctions of immiscible CO2 huff and puff performance in Chang-7 tight sandstone oil reservoir by applying NMR, microscope and reservoir simulation. J. Pet. Sci. Eng. 2022, 209, 109719. [Google Scholar] [CrossRef]
  34. Sun, Z.; Wang, X.J. Theoretical Research in Laboratory and Field Trial of Micro-Nano-Oil Displacement System Conformance Control Technology. Lithosphere 2022, 2022, 8325652. [Google Scholar] [CrossRef]
  35. Rana, A.K.; Thakur, M.K.; Gupta, V.K.; Thakur, V.K. Exploring the role of nanocellulose as potential sustainable material for enhanced oil recovery: New paradigm for a circular economy. Process Saf. Environ. Prot. 2024, 183, 1198–1222. [Google Scholar] [CrossRef]
  36. Bai, Y.; Pu, C.; Li, X.; Huang, F.; Liu, S.; Liang, L.; Liu, J. Performance evaluation and mechanism study of a functionalized silica nanofluid for enhanced oil recovery in carbonate reservoirs. Colloids Surf. A-Physicochem. Eng. Asp. 2022, 652, 129939. [Google Scholar] [CrossRef]
  37. Wu, C.; Ye, Z.; Qiao, D.; Wang, J.; Tang, L.; Lai, N. Preparation of SiO2-Fluorinated Acrylate Polymer Nanoemulsions (SCFs) and Their Application as Depressurization and Injection Treatment Agents. Energy Fuels 2023, 37, 350–359. [Google Scholar] [CrossRef]
  38. Jafarbeigi, E.; Mansouri, M.; Talebian, S.H. Effect of UiO-66-NH2/TiO2 nano-fluids on the IFT reduction and their use for wettability alteration of carbonate rocks. Mater. Chem. Phys. 2023, 299, 127496. [Google Scholar] [CrossRef]
  39. Aadland, R.C.; Jakobsen, T.D.; Heggset, E.B.; Long-Sanouiller, H.; Simon, S.; Paso, K.G.; Syverud, K.; Torsæter, O. High-Temperature Core Flood Investigation of Nanocellulose as a Green Additive for Enhanced Oil Recovery. Nanomaterials 2019, 9, 665. [Google Scholar] [CrossRef]
  40. Yin, D.; Li, Q.; Zhao, D.; Huang, T. Modified silica nanoparticles stabilized foam for enhanced oil recovery. Front. Energy Res. 2024, 12, 1386538. [Google Scholar] [CrossRef]
  41. Surawathanawises, K.; Wiedorn, V.; Cheng, X.H. Micropatterned macroporous structures in microfluidic devices for viral separation from whole blood. Analyst 2017, 142, 2220–2228. [Google Scholar] [CrossRef]
  42. Lin, W.; Li, X.; Yang, Z.; Xiong, S.; Luo, Y.; Zhao, X. Modeling of 3D Rock Porous Media by Combining X-ray CT and Markov Chain Monte Carlo. J. Energy Resour. Technol.-Trans. ASME 2020, 142, 013001. [Google Scholar] [CrossRef]
  43. Ba-Abbad, M.M.; Benamour, A.; Ewis, D.; Mohammad, A.W.; Mahmoudi, E. Synthesis of Fe3O4 nanoparticles with different shapes through a co-precipitation method and their application. JOM 2022, 74, 3531–3539. [Google Scholar] [CrossRef]
  44. Mittal, V.; Matsko, N.B.; Butté, A.; el, al. Functionalized polystyrene latex particles as substrates for ATRP: Surface and colloidal characterization. Polymer. 2007, 48, 2806–2817. [Google Scholar] [CrossRef]
  45. Liao, J.; Hu, H.; Fu, W.; Li, S.; Chen, Q. A hydrometallurgical route to produce ZnO nanoparticles and NiO strips from the spent Ni/ZnO catalyst. Hydrometallurgy 2012, 121, 107–115. [Google Scholar] [CrossRef]
  46. Zhao, Y.; Wang, Y.; Ran, F.; Cui, Y.; Liu, C.; Zhao, Q.; Gao, Y.; Wang, D.; Wang, S. A comparison between sphere and rod nanoparticles regarding their in vivo biological behavior and pharmacokinetics. Sci. Rep. 2017, 7, 4131. [Google Scholar] [CrossRef]
  47. Zheng, X.; Chen, L.; Tan, J.; Miao, J.; Liu, X.; Yang, T.; Ding, Z. Effect of micro/nano-sheet array structures on the osteo-immunomodulation of macrophages. Regen. Biomater. 2022, 9, rbac075. [Google Scholar] [CrossRef]
  48. Cheraghian, G.; Rostami, S.; Afrand, M. Nanotechnology in enhanced oil recovery. Processes 2020, 8, 1073. [Google Scholar] [CrossRef]
  49. Razavi, S.; Hernandez, L.M.; Read, A.; Vargas, W.L.; Kretzschmar, I. Surface tension anomaly observed for chemically-modified Janus particles at the air/water interface. J. Colloid Interface Sci. 2020, 558, 95–99. [Google Scholar] [CrossRef]
  50. Eltoum, H.; Yang, Y.; Hou, J. The effect of nanoparticles on reservoir wettability alteration: A critical review. Pet. Sci. 2021, 18, 136–153. [Google Scholar] [CrossRef]
  51. Ahmadi, Y.; Mohammadi, M.; Sedighi, M. Introduction to Chemical Enhanced Oil Recovery; Elsevier: Amsterdam, The Netherlands, 2022; pp. 1–32. [Google Scholar]
  52. Anto-Darkwah, E.; Rabinovich, A. Modeling imbibition coreflooding in heterogeneous cores with sub-core scale hysteresis. Adv. Water Resour. 2022, 164, 104214. [Google Scholar] [CrossRef]
  53. Huang, K.; Zhu, W.; Sun, L.; Wang, Q.; Liu, Q. Experimental study on gas EOR for heavy oil in glutenite reservoirs after water flooding. J. Pet. Sci. Eng. 2019, 181, 106130. [Google Scholar] [CrossRef]
  54. Song, H.; Lao, J.; Zhang, L.; Xie, C.; Wang, Y. Underground hydrogen storage in reservoirs: Pore-scale mechanisms and optimization of storage capacity and efficiency. Appl. Energy 2023, 337, 120901. [Google Scholar] [CrossRef]
  55. Song, H.; Zhang, J.; Sun, Y.; Li, Y.; Zhang, X.; Ma, D.; Kou, J. Theoretical Study on Thermal Release of Helium-3 in Lunar Ilmenite. Minerals 2021, 11, 319. [Google Scholar] [CrossRef]
  56. Du, S.; Wang, J.; Wang, M.; Yang, J.; Zhang, C.; Zhao, Y.; Song, H. A systematic data-driven approach for production forecasting of coalbed methane incorporating deep learning and ensemble learning adapted to complex production patterns. Energy 2023, 263, 126121. [Google Scholar] [CrossRef]
  57. Mahmoudi, M.; Landry, M.P.; Moore, A.; Coreas, R. The protein corona from nanomedicine to environmental science. Nat. Rev. Mater. 2023, 8, 422–438. [Google Scholar] [CrossRef]
  58. Wang, F.Y.; Zhao, J.Y. A mathematical model for co-current spontaneous water imbibition into oil-saturated tight sandstone: Upscaling from pore-scale to core-scale with fractal approach. J. Pet. Sci. Eng. 2019, 178, 376–388. [Google Scholar] [CrossRef]
  59. Wang, L.; Li, Z.; Lu, T.; Lai, F. Experimental verification of the effects of three metal oxide nanoparticles on mass transfer at gas-liquid interface. J. Pet. Sci. Eng. 2022, 211, 110122. [Google Scholar] [CrossRef]
  60. Wu, Y.; Tang, L.; Cao, M.; Li, L.; Liu, K.; Kong, D.; Zhao, Q.; Jin, X.; Liu, H.; Dai, C. Facile and controllable synthesis of amino-modified carbon dots for efficient oil displacement. Nano Res. 2023, 16, 6048–6056. [Google Scholar] [CrossRef]
  61. Wang, K.; You, Q.; Long, Q.M.; Zhou, B.; Wang, P. Experimental study of the mechanism of nanofluid in enhancing the oil recovery in low permeability reservoirs using microfluidics. Pet. Sci. 2023, 20, 382–395. [Google Scholar] [CrossRef]
  62. Gao, R.; Huang, Y.; Gan, W.; Xiao, S.; Gao, Y.; Fang, B.; Zhang, X.; Lyu, B.; Huang, R.; Li, J.; et al. Superhydrophobic elastomer with leaf-spring microstructure made from natural wood without any modification chemicals. Chem. Eng. J. 2022, 442, 136338. [Google Scholar] [CrossRef]
  63. Xu, D.D.; Zheng, X.T.; Xiao, R. Hydrophilic nanofibrous composite membrane prepared by melt-blending extrusion for effective separation of oil/water emulsion. RSC Adv. 2017, 7, 7108–7115. [Google Scholar] [CrossRef]
  64. Asfaram, A.; Sadeghi, H.; Goudarzi, A.; Kokhdan, E.P.; Salehpour, Z. Ultrasound combined with manganese-oxide nanoparticles loaded on activated carbon for extraction and pre-concentration of thymol and carvacrol in methanolic extracts of Thymus daenensis, Salvia officinalis, Stachys pilifera, Satureja khuzistanica, and mentha, and water samples. Analyst 2019, 144, 1923–1934. [Google Scholar]
  65. Liu, P.; Zhang, Y.; Liu, S.; Zhang, Y.; Du, Z.; Qu, L. Bio-inspired fabrication of fire-retarding, magnetic-responsive, superhydrophobic sponges for oil and organics collection. Appl. Clay Sci. 2019, 172, 19–27. [Google Scholar] [CrossRef]
  66. Romasanta, L.J.; D’alençon, L.; Kirchner, S.; Pradère, C.; Leng, J. Thin Coatings of Cerium Oxide Nanoparticles with Anti-Reflective Properties. Appl. Sci. 2019, 9, 3886. [Google Scholar] [CrossRef]
  67. Bahraminejad, H.; Manshad, A.K.; Keshavarz, A.; Iglauer, S.; Sajadi, S.M. Nanocomposite synergy for enhanced oil recovery: Macro and micro analysis of CeO2/montmorillonite nanofluids in carbonate and sandstone reservoirs. Fuel 2024, 360, 130384. [Google Scholar] [CrossRef]
  68. Panahpoori, D.; Rezvani, H.; Parsaei, R.; Riazi, M. A pore-scale study on improving CTAB foam stability in heavy crude oil-water system using TiO2 nanoparticles. J. Pet. Sci. Eng. 2019, 183, 106411. [Google Scholar] [CrossRef]
  69. Huang, X.; Sun, J.; Li, H.; Wang, R.; Lv, K.; Li, H. Fabrication of a Hydrophobic Hierarchical Surface on Shale Using Modified Nano-SiO2 for Strengthening the Wellbore Wall in Drilling Engineering. Engineering 2022, 11, 101–110. [Google Scholar] [CrossRef]
  70. Jin, J.; Wang, Y.; Nguyen, T.A.; Nguyen, A.V.; Wei, M.; Bai, B. The effect of gas-wetting nano-particle on the fluid flowing behavior in porous media. Fuel 2017, 196, 431–441. [Google Scholar] [CrossRef]
  71. Guo, B.Y.; Zhang, P.; Liu, N. Mathematical modeling of nano-particle transport in oil well cement cracks. J. Pet. Sci. Eng. 2022, 217, 110862. [Google Scholar] [CrossRef]
  72. Tanaka, Y.; Amaya, S.; Funano, S.I.; Sugawa, H.; Nagafuchi, W.; Ito, Y.; Aishan, Y.; Liu, X.; Kamamichi, N.; Yalikun, Y. A pressure driven electric energy generator exploiting a micro- to nano-scale glass porous filter with ion flow originating from water. Sci. Rep. 2022, 12, 16827. [Google Scholar] [CrossRef]
  73. Wu, J.; Zhang, S.; Zeng, L.; Nie, Q.; Ma, L.; Wang, H. The desorption of oil from nano-micro pores of shale cuttings based on particle self-rotation in the cyclone. Sep. Purif. Technol. 2022, 298, 121256. [Google Scholar] [CrossRef]
  74. AfzaliTabar, M.; Alaei, M.; Bazmi, M.; Khojasteh, R.R.; Koolivand-Salooki, M.; Motiee, F.; Rashidi, A.M. Facile and economical preparation method of nanoporous graphene/silica nanohybrid and evaluation of its Pickering emulsion properties for Chemical Enhanced oil Recovery (C-EOR). Fuel 2017, 206, 453–466. [Google Scholar] [CrossRef]
  75. Wu, S.; Tian, S.; Jian, R.; Wu, T.N.; Milazzo, T.D.; Luo, T.; Xiong, G. Graphene petal foams with hierarchical micro- and nano-channels for ultrafast spontaneous and continuous oil recovery. J. Mater. Chem. A 2022, 10, 11651–11658. [Google Scholar] [CrossRef]
  76. Muñiz-Serrato, O.; Serrato-Rodríguez, J. Formation of flow coated high catalytic activity thin films from the low temperature sol-gel titanium butoxide precursor. J. Ceram. Process. Res. 2018, 19, 306–310. [Google Scholar]
  77. Mao, H.; Qiu, Z.; Shen, Z.; Huang, W. Hydrophobic associated polymer based silica nanoparticles composite with core-shell structure as a filtrate reducer for drilling fluid at utra-high temperature. J. Pet. Sci. Eng. 2015, 129, 1–14. [Google Scholar] [CrossRef]
  78. Kazemzadeh, Y.; Sourani, S.; Doryani, H.; Reyhani, M.; Shabani, A.; Fallah, H. Recovery of Asphaltenic Oil During Nano Fluid Injection. Pet. Sci. Technol. 2015, 33, 139–146. [Google Scholar] [CrossRef]
  79. Zhang, L.; Li, X.F. Facile preparation of honeycomb-structured TiO2 nanofilm via breath figures assembly and coffee ring effect. Mater. Lett. 2018, 227, 74–77. [Google Scholar] [CrossRef]
  80. Wu, T.; Xu, W.H.; Guo, K.; Xie, H.; Qu, J.P. Efficient fabrication of lightweight polyethylene foam with robust and durable superhydrophobicity for self-cleaning and anti-icing applications. Chem. Eng. J. 2021, 407, 127100. [Google Scholar] [CrossRef]
  81. Martín-Alfonso, M.A.; Martín-Alfonso, J.E.; Rubio-Valle, J.F.; Hinestroza, J.P.; Franco, J.M. Tunable architectures of electrospun cellulose acetate phthalate applied as thickeners in green semisolid lubricants. Appl. Mater. Today 2024, 36, 102030. [Google Scholar] [CrossRef]
  82. Zhang, W.; Zhang, F.; Gao, S.; Zhu, Y.; Li, J.; Jin, J. Micro/nano hierarchical poly(acrylic acid)-grafted-poly(vinylidene fluoride) layer coated foam membrane for temperature-controlled separation of heavy oil/water. Sep. Purif. Technol. 2015, 156, 207–214. [Google Scholar] [CrossRef]
  83. Sun, Z.; Li, M.; Yuan, S.; Hou, X.; Bai, H.; Zhou, F.; Liu, X.; Yang, M. The flooding mechanism and oil recovery of nanoemulsion on the fractured/non-fractured tight sandstone based on online LF-NMR experiments. Energy 2024, 291, 130226. [Google Scholar] [CrossRef]
  84. Fontana, F.; Lindstedt, H.; Correia, A.; Chiaro, J.; Kari, O.K.; Ndika, J.; Alenius, H.; Buck, J.; Sieber, S.; Mäkilä, E.; et al. Influence of Cell Membrane Wrapping on the Cell-Porous Silicon Nanoparticle Interactions. Adv. Healthc. Mater. 2020, 9, 2000529. [Google Scholar] [CrossRef]
  85. Li, Q.; Yu, X.; Wang, L.; Qu, S.; Wu, W.; Ji, R.; Luo, Y.; Yang, H. Nano-silica hybrid polyacrylamide/polyethylenimine gel for enhanced oil recovery at harsh conditions. Colloids Surf. A-Physicochem. Eng. Asp. 2022, 633, 127898. [Google Scholar] [CrossRef]
  86. Gao, Q.; Cheng, S.; Wang, X.; Tang, Y.; Yuan, Y.; Li, A.; Guan, S. Three-dimensional hierarchical nanostructured porous epoxidized natural rubber latex/poly(vinyl alcohol) material for oil/water separation. J. Appl. Polym. Sci. 2022, 139, e52825. [Google Scholar] [CrossRef]
  87. Gui, W.; Liang, Y.; Hao, G.; Lin, J.; Sun, D.; Liu, M.; Liu, C.; Zhang, H. High Nb-TiAl-based porous composite with hierarchical micro-pore structure for high temperature applications. J. Alloys Compd. 2018, 744, 463–469. [Google Scholar] [CrossRef]
  88. Zhan, Y.; He, S.; Wan, X.; Zhao, S.; Bai, Y. Thermally and chemically stable poly(arylene ether nitrile)/halloysite nanotubes intercalated graphene oxide nanofibrous composite membranes for highly efficient oil/water emulsion separation in harsh environment. J. Membr. Sci. 2018, 567, 76–88. [Google Scholar] [CrossRef]
  89. Zhu, W.Y.; Ma, Q.P.; Han, H.Y. Theoretical study on profile control of a nano-microparticle dispersion system based on fracture-matrix dual media by a low-permeability reservoir. Energy Rep. 2021, 7, 1488–1500. [Google Scholar] [CrossRef]
  90. Lu, T.; Li, Z.; Zhou, Y.; Zhang, C. Enhanced oil recovery of low-permeability cores by SiO2 nanofluid. Energy Fuels 2017, 31, 5612–5621. [Google Scholar] [CrossRef]
  91. Li, Q.; Wei, B.; Lu, L.; Li, Y.; Wen, Y.; Pu, W.; Li, H.; Wang, C. Investigation of physical properties and displacement mechanisms of surface-grafted nano-cellulose fluids for enhanced oil recovery. Fuel 2017, 207, 352–364. [Google Scholar] [CrossRef]
  92. Gentile, F.; Coluccio, M.L.; Zaccaria, R.P.; Francardi, M.; Cojoc, G.; Perozziello, G.; Raimondo, R.; Candeloro, P.; Di Fabrizio, E. Selective on site separation and detection of molecules in diluted solutions with super-hydrophobic clusters of plasmonic nanoparticles. Nanoscale 2014, 6, 8208–8225. [Google Scholar] [CrossRef] [PubMed]
  93. Gu, X.; Pu, C.; Huang, H.; Huang, F.; Li, Y.; Liu, Y.; Liu, H. Micro-influencing mechanism of permeability on spontaneous imbibition recovery for tight sandstone reservoirs. Pet. Explor. Dev. 2017, 44, 1003–1009. [Google Scholar] [CrossRef]
  94. Jia, H.; Dai, J.; Miao, L.; Wei, X.; Tang, H.; Huang, P.; Jia, H.; He, J.; Lv, K.; Liu, D. Potential application of novel amphiphilic Janus-SiO2 nanoparticles stabilized O/W/O emulsion for enhanced oil recovery. Colloids Surf. A: Physicochem. Eng. Asp. 2021, 622, 126658. [Google Scholar] [CrossRef]
  95. Giraldo, L.J. Janus nanoparticles for enhanced oil recovery EOR: Reduction of interfacial tension. In Proceedings of the SPE Annual Technical Conference and Exhibition, Dallas, TX, USA, 24–26 September 2018. [Google Scholar]
  96. Li, L.; Sun, Y.; Li, Y.; Wang, R.; Chen, J.; Wu, Y.; Dai, C. Interface properties evolution and imbibition mechanism of gel breaking fluid of clean fracturing fluid. J. Mol. Liq. 2022, 359, 118952. [Google Scholar] [CrossRef]
  97. Raventhiran, N.; Molla, R.S.; Nandishwara, K.; Johnson, E.; Li, Y. Design and fabrication of a novel on-chip pressure sensor for microchannels. Lab A Chip 2022, 22, 4306–4316. [Google Scholar] [CrossRef]
  98. Wu, C.; Ye, Z.; Nie, X.; Liu, D.; Lai, N. Synthesis and evaluation of depressurization and injection treatment agent suitable for low-permeability reservoirs. Chem. Phys. Lett. 2022, 804, 139904. [Google Scholar] [CrossRef]
  99. Hu, Q.; Zhang, Y.; Meng, X.; Li, Z.; Xie, Z.; Li, M. Characterization of micro-nano pore networks in shale oil reservoirs of Paleogene Shahejie Formation in Dongying Sag of Bohai Bay Basin, East China. Pet. Explor. Dev. 2017, 44, 720–730. [Google Scholar] [CrossRef]
  100. Liang, H.; Kuang, Q.; Hu, C.; Chen, J.; Lu, X.; Huang, Y.; Yan, H. Construction of durable superhydrophobic and anti-icing coatings via incorporating boroxine cross-linked silicone elastomers with good self-healability. Soft Matter 2022, 18, 8238–8250. [Google Scholar] [CrossRef]
  101. Yang, L.; Yang, J.; Gao, J.; Zhang, X. The Characteristics of Oil Occurrence and Long-Distance Transportation due to Injected Fluid in Tight Oil Reservoirs. Adv. Polym. Technol. 2019, 2019, 2707616. [Google Scholar] [CrossRef]
  102. Gheorghita, G.R.; Paun, V.I.; Neagu, S.; Maria, G.M.; Enache, M.; Purcarea, C.; Parvulescu, V.I.; Tudorache, M. Cold-Active Lipase-Based Biocatalysts for Silymarin Valorization through Biocatalytic Acylation of Silybin. Catalysts 2021, 11, 1390. [Google Scholar] [CrossRef]
  103. De Caprariis, B.; Bracciale, M.P.; Bavasso, I.; Chen, G.; Damizia, M.; Genova, V.; Marra, F.; Paglia, L.; Pulci, G.; Scarsella, M.; et al. Unsupported Ni metal catalyst in hydrothermal liquefaction of oak wood: Effect of catalyst surface modification. Sci. Total Environ. 2020, 709, 136215. [Google Scholar] [CrossRef]
  104. Cao, W.; Ma, W.; Lu, T.; Jiang, Z.; Xiong, R.; Huang, C. Multifunctional nanofibrous membranes with sunlight-driven self-cleaning performance for complex oily wastewater remediation. J. Colloid Interface Sci. 2022, 608, 164–174. [Google Scholar] [CrossRef] [PubMed]
  105. Bai, Y.; Lian, Y.; Zhao, J.; Cao, Z.; Sun, J.; Zhang, H. Thermal-insulation and temperature-resistant foamed gel for thermal management of heavy oil steam flooding. J. Mol. Liq. 2022, 359, 119304. [Google Scholar] [CrossRef]
  106. Jawad, M. Insinuation of arrhenius energy and solar radiation on electrical conducting williamson nano fluids flow with swimming microorganism: Completion of buongiorno’s model. East Eur. J. Phys. 2023, 135–145. [Google Scholar] [CrossRef]
  107. Ren, Q.; Dai, T.; Jin, X.; Wu, D.; Wang, C.; Li, J.; Zhu, S. Solution Processed Coating of Polyolefin on Melamine Foams to Fabricate Tough Oil Superabsorbents. Macromol. Mater. Eng. 2018, 303, 1800436. [Google Scholar] [CrossRef]
  108. Kumasaka, J.; Kaito, Y.; Goto, A.; Ito, D.; Kitagawa, H.; Nogami, T.; Murakami, S. First Nanoparticle-Based EOR Project in Japan: Field Pilot Test. In Proceedings of the SPE Improved Oil Recovery Conference, Tulsa, OK, USA, 23–24 April 2024. [Google Scholar]
  109. Franco, C.A.; Giraldo, L.J.; Candela, C.H.; Bernal, K.M.; Villamil, F.; Montes, D.; Lopera, S.H.; Franco, C.A.; Cortés, F.B. Design and tuning of nanofluids applied to chemical enhanced oil recovery based on the surfactant–nanoparticle–brine interaction: From laboratory experiments to oil field application. Nanomaterials 2020, 10, 1579. [Google Scholar] [CrossRef]
  110. Franco, C.A.; Franco, C.A.; Zabala, R.D.; Bahamón, I.; Forero, A.; Cortés, F.B. Field Applications of nanotechnology in the oil and gas industry: Recent advances and perspectives. Energy Fuels 2021, 35, 19266–19287. [Google Scholar] [CrossRef]
  111. Kanj, M.Y.; Rashid, M.H.; Giannelis, E.P. Industry first field trial of reservoir nanoagents. In Proceedings of the SPE Middle East Oil and Gas Show and Conference, Manama, Bahrain, 25–28 September 2011. [Google Scholar]
  112. Hendraningrat, L.; Majidaie, S.; Kechut, N.I.; Tewari, R.D.; Sedaralit, M.F.; Skoreyko, F.; MousaviMirkalaei, S.M. Novel Robust Three-Dimensional Field-Scale Reservoir Modeling Development of Nanoparticles for Improved and Enhanced Oil Recovery. Energy Fuels 2023, 37, 18666–18683. [Google Scholar] [CrossRef]
  113. Irfan, S.A.; Shafie, A.; Yahya, N.; Zainuddin, N. Mathematical modeling and simulation of nanoparticle-assisted enhanced oil recovery—A review. Energies 2019, 12, 1575. [Google Scholar] [CrossRef]
  114. Worthen, A.; Taghavy, A.; Aroonsri, A.; Kim, I.; Johnston, K.; Huh, C.; Bryant, S.; DiCarlo, D. Multi-scale evaluation of nanoparticle-stabilized CO2-in-water foams: From the benchtop to the field. In Proceedings of the SPE Annual Technical Conference and Exhibition, Houston, TX, USA, 28–30 September 2015; p. D011S009R006. [Google Scholar]
  115. Loaiza, C.S.; Patiño, J.F.; Mejía, J.M. Numerical evaluation of a combined chemical enhanced oil recovery process with polymer and nanoparticles based on experimental observations. J. Pet. Sci. Eng. 2020, 191, 107166. [Google Scholar] [CrossRef]
  116. Pal, N.; Mandal, A. Numerical simulation of enhanced oil recovery studies for aqueous gemini surfactant-polymer-nanoparticle systems. AIChE J. 2020, 66, e17020. [Google Scholar] [CrossRef]
  117. Safari, M.; Golsefatan, A.; Rezaei, A.; Jamialahmadi, M. Simulation of silica nanoparticle flooding for enhancing oil recovery. Pet. Sci. Technol. 2015, 33, 152–158. [Google Scholar] [CrossRef]
  118. Pal, N.; Mandal, A. Compositional simulation model and history-matching analysis of surfactant-polymer-nanoparticle (SPN) nanoemulsion assisted enhanced oil recovery. J. Taiwan Inst. Chem. Eng. 2021, 122, 1–13. [Google Scholar] [CrossRef]
  119. Wei, B.; Li, Q.; Jin, F.; Li, H.; Wang, C. The potential of a novel nanofluid in enhancing oil recovery. Energy Fuels 2016, 30, 2882–2891. [Google Scholar] [CrossRef]
  120. Li, R.; Jiang, P.; Gao, C.; Huang, F.; Xu, R.; Chen, X. Experimental investigation of silica-based nanofluid enhanced oil recovery: The effect of wettability alteration. Energy Fuels 2017, 31, 188–197. [Google Scholar] [CrossRef]
  121. Zhang, H.; Ramakrishnan, T.S.; Nikolov, A.; Wasan, D. Enhanced oil recovery driven by nanofilm structural disjoining pressure: Flooding experiments and microvisualization. Energy Fuels 2016, 30, 2771–2779. [Google Scholar] [CrossRef]
  122. Hendraningrat, L.; Shidong, L.; Torsœter, O. A glass micromodel experimental study of hydrophilic nanoparticles retention for EOR project. In Proceedings of the SPE Russian Petroleum Technology Conference, Moscow, Russia, 16–18 October 2012. [Google Scholar]
  123. Mohajeri, M.; Hemmati, M.; Shekarabi, A.S. An experimental study on using a nanosurfactant in an EOR process of heavy oil in a fractured micromodel. J. Pet. Sci. Eng. 2015, 126, 162–173. [Google Scholar] [CrossRef]
  124. Cheraghian, G. Improved heavy oil recovery by nanofluid surfactant flooding-an experimental study. In Proceedings of the 78th EAGE Conference and Exhibition 2016, Vienna, Austria, 29–30 May 2016. [Google Scholar]
  125. Hashemi, A.; Borazjani, S.; Nguyen, C.; Loi, G.; Khazali, N.; Badalyan, A.; Yang, Y.; Dang-Le, B.; Russell, T.; Bedrikovetsky, P. Particle detachment in reservoir flows by breakage due to induced stresses and drag. Int. J. Rock Mech. Min. Sci. 2023, 172, 105591. [Google Scholar] [CrossRef]
  126. Dinariev, O.Y.; Rego, L.P.; Bedrikovetsky, P. Probabilistic averaging in kinetic theory for colloidal transport in porous media. J. Comput. Appl. Math. 2022, 403, 113840. [Google Scholar] [CrossRef]
  127. Russell, T.; Bedrikovetsky, P. Boltzmann’s colloidal transport in porous media with velocity-dependent capture probability. Phys. Fluids 2021, 33, 053306. [Google Scholar] [CrossRef]
  128. Du, S.; Wang, M.; Yang, J.; Zhao, Y.; Wang, J.; Yue, M.; Xie, C.; Song, H. A novel prediction method for coalbed methane production capacity combined extreme gradient boosting with bayesian optimization. Comput. Geosci. 2023, 1–10. [Google Scholar] [CrossRef]
  129. Du, S.; Wang, M.; Yang, J.; Zhao, Y.; Wang, J.; Yue, M.; Xie, C.; Song, H. An enhanced prediction framework for coalbed methane production incorporating deep learning and transfer learning. Energy 2023, 282, 128877. [Google Scholar] [CrossRef]
Figure 1. Review framework of micro/nanoparticular flow in porous media for EOR considering particle materials and designs [43,44,45,46,47], EOR mechanisms [48,49,50], research schemes [51,52,53,54,55], and future directions [56,57].
Figure 1. Review framework of micro/nanoparticular flow in porous media for EOR considering particle materials and designs [43,44,45,46,47], EOR mechanisms [48,49,50], research schemes [51,52,53,54,55], and future directions [56,57].
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Figure 3. EOR mechanisms of micro/nanoparticles. (a) Profile control of a nano-microparticle [89], (b1,b2) nanoparticles plugging the pore throats [48,90], (c) wettability alteration by nanoparticles [91], (d) interfacial tension reduction by nanoparticles [74].
Figure 3. EOR mechanisms of micro/nanoparticles. (a) Profile control of a nano-microparticle [89], (b1,b2) nanoparticles plugging the pore throats [48,90], (c) wettability alteration by nanoparticles [91], (d) interfacial tension reduction by nanoparticles [74].
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Figure 4. Research progress on leveraging micro/nanoparticles for EOR. (a) Microfluidic experiments for visualizing EOR effect after alternative water–nanofluid injections [119], (b) microfluidic comparisons of EOR performances by water flooding and nanofluid flooding [120], (c) core simulation of EOR effect after nanofluid flooding [121].
Figure 4. Research progress on leveraging micro/nanoparticles for EOR. (a) Microfluidic experiments for visualizing EOR effect after alternative water–nanofluid injections [119], (b) microfluidic comparisons of EOR performances by water flooding and nanofluid flooding [120], (c) core simulation of EOR effect after nanofluid flooding [121].
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Table 1. Summary of the materials, sizes, structural designs, fabrication techniques, EOR mechanisms, and EOR performances of micro/nanoparticles.
Table 1. Summary of the materials, sizes, structural designs, fabrication techniques, EOR mechanisms, and EOR performances of micro/nanoparticles.
MaterialSizeFabricationStructure DesignFunctionMechanismEOR
Silica10–100 nmSol–gel processSpherical, RodsInterface regulationAlters rock wettability, reduces IFT15–25%
Titanium Dioxide (TiO2)5–50 nmHydrothermal synthesisSpherical, RodsPhotocatalytic activityDecomposes organic materials, reduces viscosity, acts as a catalyst20–50%
Iron Oxide (Fe3O4)10–100 nmCo-precipitationSpherical, Cubic, MagneticMagnetic properties, increased mobilityAids in tracing and targeting, reduces viscosity20–30%
Zinc Oxide (ZnO)10–50 nmChemical vapor depositionSpherical, RodsCatalytic propertiesEnhances chemical reactions, reduces viscosity, acts as a reagent with oil18–25%
Silver10–100 nmChemical reductionSpherical, CubicAntimicrobial propertiesPrevents microbial-induced souring, acts as a catalyst12–18%
Gold1–100 nmCitrate reductionSpherical, RodsCatalytic properties, increased stabilityAlters wettability, improves sweep efficiency, acts as a catalyst15–25%
Carbon Nanotube1–10 nmChemical vapor depositionTubular, Multi-walledHigh surface area, strengthAlters wettability and IFT, enhances sweep efficiency22–32%
Graphene Oxide1–20 nmHummers’ methodLayered, SheetsHigh surface area, conductivityAlters wettability, reduces IFT18–28%
Polymer-coated10–100 nmEmulsion polymerizationCore-shell, SphericalIncreased stability, functionalizationAlters wettability, reduces IFT, acts as a reagent with oil20–30%
Polymers50–200 nmEmulsion polymerization, precipitation polymerizationSpherical, NetworkEnhance viscosity, improve sweep efficiencyIncreases water viscosity, enhances sweep efficiency, improves mobility control20–35%
Nanoemulsions20–200 nmUltrasonication, high-pressure homogenization, spontaneous emulsificationDroplets, Mixed-phaseEnhance oil mobilization, reduce viscosity, improve sweep efficiencyReduces IFT, stabilizes emulsion, disperses oil droplets uniformly25–40%
Table 2. Summary of research scales, methods, schemes, goals, advantages, and limitations of micro/nanoparticle based EOR.
Table 2. Summary of research scales, methods, schemes, goals, advantages, and limitations of micro/nanoparticle based EOR.
ScaleMethodSchemeGoalAdvantageLimitation
FieldModeling and simulationDarcy LawEOR, flow field predictionWell-established, simple to applyAssumes homogeneity, limited accuracy in complex reservoirs
Reservoir simulation software (ECLIPSE version 300, CMG version 2023.1)EOR strategy development, production optimizationComprehensive, considers geological and operational dataRequires detailed input data, can be computationally intensive
ExperimentPilot testingValidate EOR methods, scale-up evaluationReal-world validation, informs full-field applicationExpensive, long duration, risk of non-representative results
CoreModeling and simulationLattice Boltzmann Method (LBM)Flow field prediction, EORCaptures pore-scale/meso-scale phenomena, adaptableHigh computational cost, complex setup
Finite Element/Volume Method (FEM/FVM)Flow field prediction, EOR, Stress and deformation analysisAccurate for complex geometries, versatileComputationally intensive, requires fine meshing
ExperimentCore flooding with Computed Tomography (CT)/Computed Tomography (NMR)Revealing mechanisms, EOR, Pore-scale visualizationVisual observation, detailed analysis, Non-destructive, high-resolutionExpensive, limited by sample size
Micro/PoreModeling and simulationNavier–Stokes (N-S) equationsFlow field prediction, understanding flow dynamicsAccurate for complex flows, high fidelityComputationally intensive, requires fine discretization
Lattice Boltzmann Method (LBM)Flow field prediction, pore-scale fluid dynamicsCaptures detailed flow phenomena, flexible boundary conditionsHigh computational cost, complex setup
ExperimentMicrofluidic modelsRevealing mechanisms, EOR, Microscale visualizationHigh control over experimental conditions, real-time observationScale-up issues, material compatibility
MolecularModeling and simulationMolecular Dynamics (MD)Molecular interactions, nano-scale flow dynamicsDetailed molecular insights, accurate at the atomic levelHigh computational cost, limited to very small systems
Density Functional Theory (DFT)Electronic structure calculations, chemical reactionsHigh accuracy for quantum mechanical propertiesExtremely computationally intensive, limited to small systems
ExperimentNuclear Magnetic Resonance (NMR)/Raman spectroscopyMolecular structure determination, interaction analysisDetailed molecular information, High sensitivity, non-destructiveRequires large samples, expensive equipment, limited to vibrational transitions
Table 3. Summary of recent bottleneck of EOR, future directions, and big pictures of the future advanced technology.
Table 3. Summary of recent bottleneck of EOR, future directions, and big pictures of the future advanced technology.
Bottleneck of EORDetailsFuture DirectionBig Picture
High production costs and economic feasibilityHigh costs associated with the production, modification, and deployment of specialized nanoparticles, along with extensive post-injection monitoringAI-accelerated particle designAI can reduce costs by optimizing particle properties and production methods through simulations and machine learning models
Complex reservoir heterogeneityGeological variations in reservoirs affect the distribution and efficacy of injected particles, making outcomes difficult to predict and generalize across different fieldsBig-data driven approachesUtilizing big data can help in understanding reservoir heterogeneity better, leading to more tailored EOR strategies
Environmental concerns and sustainabilityNanoparticles such as metal oxides and heavy metals can be toxic. Their long-term persistence, potential for bioaccumulation, and the environmental impact of production and disposal are major concernsBio-based schemes, non-toxic and biocompatible formulations, comprehensive lifecycle analysisBio-based nanoparticles can mitigate environmental impacts by using sustainable and biodegradable materials, while lifecycle analyses ensure minimal long-term impact from production to degradation
Particle stability under reservoir conditionsStability refers to nanoparticles’ ability to resist changes in harsh reservoir conditions. Instability can lead to particle degradation, aggregation, and reduced functionalitySmart nanoparticles responsive to reservoir conditionsSmart nanoparticles can dynamically adjust to changing reservoir conditions, improving efficiency and stability
Limited understanding of nanoparticle interactionsThe interactions between nanoparticles and reservoir fluids/rocks at the nano-scale are not fully understood, leading to unpredictable behaviors and outcomesAdvanced modeling and simulation techniquesImproved modeling can provide deeper insights into nano-scale interactions and predict macro-scale impacts more accurately
Scalability from lab to fieldSuccessful lab-scale results are often challenging to replicate at the field scale due to differences in conditions and scalesIntegrated hybrid methodsCombining chemical, thermal, microbial, and nano-enhanced techniques can create more robust and scalable EOR solutions
Energy-intensive processesThe synthesis, functionalization, and deployment of nanoparticles involve energy-intensive processes, and additional energy is required to inject micro/nanofluids into the reservoirRenewable energy-powered nanoparticle synthesisUsing renewable energy for nanoparticle production can lower the carbon footprint and enhance sustainability
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Lao, J.; Cheng, H.; Wang, Y.; Song, H. Micro/Nanoparticle Characteristics and Flow in Porous Media: A Review towards Enhanced Oil Recovery. Energies 2024, 17, 4136. https://doi.org/10.3390/en17164136

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Lao J, Cheng H, Wang Y, Song H. Micro/Nanoparticle Characteristics and Flow in Porous Media: A Review towards Enhanced Oil Recovery. Energies. 2024; 17(16):4136. https://doi.org/10.3390/en17164136

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Lao, Junming, Haoran Cheng, Yuhe Wang, and Hongqing Song. 2024. "Micro/Nanoparticle Characteristics and Flow in Porous Media: A Review towards Enhanced Oil Recovery" Energies 17, no. 16: 4136. https://doi.org/10.3390/en17164136

APA Style

Lao, J., Cheng, H., Wang, Y., & Song, H. (2024). Micro/Nanoparticle Characteristics and Flow in Porous Media: A Review towards Enhanced Oil Recovery. Energies, 17(16), 4136. https://doi.org/10.3390/en17164136

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