Toward Sustainable Green and Intelligent Profile Control Gels: An ETI–CFI-Based Structure–Environment Evaluation Framework

Abstract

In the context of the “dual-carbon” strategy and the escalating challenges posed by ultra-high water-cut reservoirs, the development of green and intelligent profile control gels (PCGs) has become essential for balancing enhanced oil recovery (EOR) efficiency with environmental sustainability. In this study, a green performance evaluation framework integrating the Environmental Toxicity Index (ETI) and Carbon Footprint Intensity (CFI) is established to quantitatively assess the environmental friendliness of polymer gel systems. Representative gel types—including conventional chromium(III)–polyacrylamide(Cr(III)–PAM), citric acid–chitosan, and pH-responsive nanogels—are evaluated to reveal their structure–environment interactions. Comparative analysis shows that the Cr(III)–PAM system exhibits strong plugging capability but imposes the highest environmental burden (ETI = 1.45; CFI = 9.1 kg CO₂e/kg), whereas the citric acid–chitosan system significantly reduces both toxicity (ETI = 0.42) and carbon footprint (CFI = 2.1). Meanwhile, pH-responsive nanogels demonstrate superior reservoir stability and sustainability under harsh conditions. The proposed ETI–CFI evaluation framework not only enables quantitative benchmarking of green performance but also provides a unified criterion for molecular design, material screening, and engineering application of intelligent green gels. This framework offers practical guidance for the low-carbon transformation of oilfield chemical systems, aligning innovation with sustainability objectives and supporting the realization of dual-carbon goals.

1. Introduction

Global energy-related CO₂ emissions have continued to rise over the past two decades, reflecting the persistent reliance of industrial and energy systems on fossil fuels. As shown in Figure 1, CO₂ emissions from major economies—including China, the United States, India, and the European Union—exhibit distinct trajectories: China’s rapid industrial expansion has driven the steepest growth since 2005, whereas emissions in the EU and Japan have gradually declined [1,2,3,4].

Complementing these trends, the IPCC AR6 WGIII Summary and the Global Carbon Budget 2023 indicate that cumulative anthropogenic CO₂ emissions since the pre-industrial era exceed ~2500 Gt CO₂, while the remaining carbon budget consistent with a 1.5 °C pathway has declined to <~300 Gt CO₂ [5,6]. According to the International Energy Agency (IEA), global fossil-energy-related CO₂ emissions reached 37.4 Gt in 2024, and more than 130 countries have incorporated carbon-reduction or carbon-neutrality targets into their national strategies [7,8]. This accelerating decarbonization momentum is reshaping the oil and gas sector toward low-carbon, environmentally adaptive technologies, such as polymer gels designed for carbon-efficient enhanced oil recovery (EOR). Nevertheless, despite rapid policy advances, the oil and gas industry remains among the most carbon-intensive sectors; process-level energy use and produced-water management are key determinants of overall carbon performance.

Figure 1. Global carbon dioxide (CO₂) emissions [9].

Figure 1. Global carbon dioxide (CO₂) emissions [9].

Under the dual-carbon framework, performance metrics in oilfield development are shifting from absolute hydrocarbon output to Carbon Footprint Intensity (CFI)—defined as life cycle CO₂ emissions per unit of crude oil produced. In line with ISO 14040/14044 [10] and ISO 14067 [11], CFI is quantified within a life cycle assessment (LCA) framework that specifies the goal-and-scope definition [12,13], system boundaries, and life cycle inventory (LCI) compilation. Impact assessment can then be performed using standard LCIA method families—such as ReCiPe [14,15], USEtox for human/ecotoxicity [16,17,18,19,20], or TRACI for North American contexts [21,22], thereby ensuring methodological consistency and comparability. This shift in evaluation standards elevates energy use, chemical consumption, and produced-water management from secondary operational concerns to core elements of the carbon-reduction agenda. As a result, the operational inefficiencies of mature oilfields—particularly the excessive energy and water circulation associated with high water-cut production—have become key barriers to achieving carbon-intensity mitigation under the dual-carbon framework. Mature reservoirs operating at ultra-high water cut are major contributors to elevated CFI, as each additional barrel of injected or produced water increases injection pressure, lifting power, and treatment load while yielding little incremental oil. Accordingly, water management serves not merely as an operational efficiency measure but as a direct lever for carbon-intensity reduction in aging reservoirs.

As a result of such water-dominated operations, many mature fields experience severe heterogeneity due to prolonged waterflooding [23,24,25,26]. To sustain production under these conditions, operators are forced to increase injection rates and processing power, leading to a feedback cycle of higher energy demand, increased chemical consumption, and elevated CFI. Thus, the challenges of mature reservoirs—high water cut, poor sweep efficiency, and escalating energy intensity—are inherently intertwined with the dual-carbon goals. Reducing ineffective water cycling and restoring displacement conformance have, therefore, become necessary preconditions for reconciling stable production with low-carbon development, rather than optional efficiency upgrades.

Profile control and water-shutoff technologies have been widely deployed to mitigate reservoir heterogeneity and reduce ineffective water production. Among these, polymer gels—owing to their tunable three-dimensional networks and controllable plugging capacity—play a pivotal role in selectively sealing high-permeability channels and improving sweep efficiency [25,27]. Water-shutoff treatments are primarily used for local sealing of dominant water-producing zones [28,29,30,31,32], whereas profile control applications aim to enhance volumetric sweep efficiency and are recognized as a core pathway for improving oil recovery [33,34]. Critically, under dual-carbon constraints, effective profile control lowers the water–oil ratio (WOR), reduces lifting and injection energy demand, decreases produced-water treatment volume, and hence helps to reduce Carbon Footprint Intensity (CFI) while improving recovery efficiency. However, conventional chemical systems—such as Cr(III)–PAM and organic crosslinked gels—exhibit a trade-off between plugging performance and environmental compatibility, which limits their suitability for low-carbon development strategies [35,36].

In recent years, emerging environmentally friendly materials—including biopolymer gels, nanocomposite gels, and multi-responsive intelligent gels—have demonstrated promising adaptability and regulatory potential in complex reservoir conditions [37]. Nevertheless, large-scale deployment still faces three critical bottlenecks: (i) fragmented evaluation methodologies, (ii) limited mechanistic understanding under coupled geological–fluid–mechanical environments, and (iii) insufficient compatibility between low-carbon materials and existing injection–production systems. These limitations manifest as structural instability, inadequate thermal/saline resistance, and insufficient consideration of degradability and life cycle environmental impact.

Overcoming these challenges requires the development of intelligent green polymer gels within a systematic framework that integrates structure design, service behavior, and sustainability. To this end, this study introduces a green grading framework based on the Environmental Toxicity Index (ETI) and Carbon Footprint Intensity (CFI). The ETI is defined as a quantitative measure of the overall ecological hazard potential of a material, derived from weighted toxicity coefficients of its constituents toward aquatic and terrestrial organisms. The CFI represents the total greenhouse gas emissions, expressed as carbon dioxide equivalent (CO₂e), associated with the life cycle production and application of the material per unit mass. The definitions, normalization rules, and scoring procedures for ETI and CFI are detailed in Section 2 (Equations (1)–(3)), including system boundaries and data sources. Figure 2 illustrates the conceptual framework, emphasizing the structural construction dimensions and green-evolution pathways of intelligent green gels.

Given these challenges, a systematic and quantitative approach is required to evaluate the environmental performance of polymer gel systems and to guide their green transformation. Building on this foundation, polymer gels are classified into four categories—conventional, low-toxicity, environmentally friendly, and intelligent green gels (

Table 1. Environmentally oriented classification and performance of polymer gels [39].

Type	Definition and Characteristics	Representative Raw Materials/Crosslinkers	Environmental Performance and Challenges
Conventional (non-green)	Synthetic polymer networks crosslinked with Cr(III) or aldehydes; high stability but poor degradability [40,41,42].	PAM with chromium/zirconium salts, glutaraldehyde.	High Cr(Ⅵ) toxicity, VOCs, and microplastic pollution; should be phased out.
Low-toxicity/low-pollution	Formulations using low-toxicity crosslinkers to reduce VOCs and improve polymer compatibility [43,44,45,46].	Citric acid, aluminum salts, organic titanium.	Lower toxicity but limited durability and aging resistance.
Green eco-friendly	Renewable polymer matrices with biodegradable crosslinkers; naturally degradable after service [47,48,49,50,51].	Alginate, chitosan, cellulose; citric or oxalic acids.	Excellent biodegradability but low salt tolerance and mechanical strength.
Intelligent green	Responsive gels with thermo-/pH-/shear-adaptive networks for targeted plugging [52,53,54].	Responsive polysaccharides or peptide-based crosslinkers.	Good adaptability; complex synthesis and high cost.

). The structural optimization and environmental adaptation mechanisms of each type are systematically analyzed, with particular emphasis on the advances of intelligent responsive gels in reservoir heterogeneity identification, dynamic plugging regulation, and multi-field coupling performance. This work aims to provide theoretical support and engineering pathways for achieving the target of chemical-flooding carbon intensity ≤1.8 t CO₂e m⁻³, as specified in the Specification for Green Mine Construction in Oilfield Development (SY/T 6788-2020) [38].

2. Results and Discussion

2.1. Development and Bottlenecks of Mainstream Gel Systems

With the continuous advancement of enhanced oil recovery (EOR) technologies, polymer gels have become a cornerstone in profile control and water-shutoff operations due to their tunable three-dimensional network structures and controllable plugging capability [55,56,57]. Despite extensive field applications, mainstream gel systems still face significant limitations under increasingly complex reservoir conditions. Their long-term performance is primarily constrained by three interrelated factors:

(i)

Limited environmental tolerance: Most conventional gels cannot remain stable under complex reservoir conditions such as high temperature, high salinity, or variable pH environments, often leading to gel syneresis, premature degradation, or insufficient plugging radius [58,59,60].

(ii)

High ecological risk: Systems based on Cr(III), Cr(Ⅵ), or phenol–formaldehyde organic crosslinkers exhibit inherent toxicity and are increasingly restricted by regulations such as the EU REACH directive [61] and China’s Measures for the Environmental Management of New Chemical Substances [62,63,64].

(iii)

Weak dynamic responsiveness: Conventional gels are typically designed for static conditions, with limited adaptability to evolving seepage structures, resulting in rapid decline of plugging efficiency during long-term reservoir development [65,66,67].

To address these challenges, recent research has shifted toward reconstructing crosslinking mechanisms and optimizing gel-network architectures, promoting the transition from conventional gels to greener, more controllable, and intelligent systems. The following subsections review the evolution and major bottlenecks of these technologies from three perspectives: metal-ion crosslinked gels, organic crosslinked gels, and dynamic plugging failure mechanisms.

2.1.1. Transition-Metal Gels: Structural Optimization and Ecological Risks

Metal-ion crosslinked gels form stable three-dimensional (3D) networks through coordination between multivalent cations and the carboxyl or hydroxyl groups of polymer chains [68]. Since the development and field deployment of Cr(III)–HPAM systems in the 1970s, these gels have been extensively used for deep-profile control and reservoir heterogeneity regulation, becoming one of the dominant plugging materials in tertiary oil recovery [69]. Nevertheless, they still face intrinsic drawbacks—rapid gelation kinetics, limited structural durability, and the ecological toxicity of heavy-metal species (notably Cr(Ⅵ))—which constrain their large-scale application and hinder their green transition.

Recent studies, therefore, aim to achieve a dual improvement in structural stability and environmental compatibility by optimizing both polymer backbones and crosslinking architectures. Under high-temperature and high-salinity (HT/HS) conditions, metal-ion gels typically undergo chain scission, ion shielding, and viscosity loss, motivating the development of multi-level reinforcement strategies. Three representative approaches have emerged: molecular modification, interfacial crosslink regulation, and nanocomposite reinforcement [45,70,71,72,73].

(i)

Backbone modification for thermo–salt resistance. Incorporating hydrophobic monomers (e.g., styrene and alkyl acrylates) induces intra- and intermolecular associations, whereas grafting polar monomers (e.g., AMPS, NVP, and guanidinium) enhances chain rigidity and hydration stability [74,75,76,77,78,79]. For instance, Gumerov et al. [80] confirmed via DPD simulations that amphiphilic PVCL/TBCHA microgels with higher hydrophobic content exhibit stronger nanoscale interfacial bonding and improved thermo-responsive resilience. Similarly, Sarsenbekuly et al. [81] developed a hydrophobically modified polyacrylamide (RH-4) that maintained viscosity under 80,000 mg L⁻¹ salinity, and Yang et al. [82] reported amphiphilic polymers maintaining > 90% volumetric stability and minimal shrinkage at 120 °C and salinity of 1.5 × 10⁵ mg/L. These results verify that molecular structure engineering is key to achieving thermo-salt-resistant backbones.

(ii)

Interfacial crosslink regulation for mechanical reinforcement. Beyond backbone stabilization, fine-tuning of crosslink density and topology effectively improves mechanical integrity. Host–guest inclusion and branched architectures enhance network compactness and self-recovery. For example, β-cyclodextrin inclusion increased crosslink density and gel strength even at low dosage in [83]; grafting acrylamide onto CMC backbones yielded rigid frameworks with >30% branching degree in [84]; and Biswas et al. [78] achieved multilayer amphiphilic gels through sequential FRP–ATRP polymerization, where interlayer covalent coupling endowed high strength and elasticity. Collectively, these studies highlight that hierarchical polymer design, rather than single-component modification, dictates 3D network deformability under HT/HS stress.

(iii)

Nanocomposite reinforcement for structural durability. To further compensate for backbone fragility, rigid–flexible hybridization using nanoparticles has gained attention. Inorganic fillers such as SiO₂, montmorillonite, and cellulose nanocrystals impart rigidity and thermal resistance, while flexible polymers (e.g., PEG and polyglutamic acid) provide elasticity and interfacial adhesion [85,86]. Recent studies have confirmed the universality of this strategy across multiple systems. Sarvesh et al. [87] incorporated Laponite^® nanoclay into ABA-type PLA–PEO–PLA hydrogels, achieving nearly an order-of-magnitude increase in storage modulus. Das et al. [88] introduced graphene nanosheets into PAM matrices to enhance tensile strength, and Yang et al. [89] fabricated core–shell SiO₂–PAM nanocomposites via in situ polymerization, markedly improving fracture strength. Hyperbranched silica nanoparticles (HBSPs) further reduced network density while preserving high deformability [90]. These results confirm that nano–macro-hierarchical coupling—rather than the mere addition of fillers—governs the enhancement of strength, elasticity, and thermal stability in metal-ion gels. Nevertheless, achieving uniform dispersion and interfacial compatibility at high nanoparticle loadings remains challenging, as agglomeration often deteriorates performance and limits large-scale application.

In summary, molecular modification, functional group grafting, and nanocomposite reinforcement have substantially improved the service stability and HT/HS tolerance of transition-metal gels (representative formulations are listed in

Table 2. Representative formulations and performance of transition-metal gels under different reinforcement strategies.

Reinforcement Strategy	Key Mechanism/Technique	Representative Formulation	Applicable T/Environment	Application Scenario	Reference
Host–guest inclusion	β-Cyclodextrin–adamantane supramolecular interaction	PAAB: PAMN = 1:1	90 °C; salinity ≈ 32,900 mg L−1	High-T, high-salinity reservoirs (EOR); drug delivery models	Zhou [91]
Thermo-responsive host–guest inclusion	Temperature-sensitive β-CD/Ad host–guest interaction; intermolecular binding energy analysis	β-CD/Ad complex	Stable at 330–340 K (57–67 °C)	Smart delivery systems; potential for thermo-responsive gels in EOR	Rasouli [92]
Zwitterionic hydrophobic monomer design	Sulfonic, phenyl, and long-alkyl monomer incorporation to enhance rigidity and salt tolerance	CQMP(AM/SSS/CQ)	Stable > 130 °C; decomposition ≈ 210 °C	High-T, high-salinity reservoirs (EOR)	Cheng [93]
Nano-fly ash composite	Nano-FA improves thermal stability and strength	AM/nano-FA/Cr(III) system	90 °C; ΔP = 0.045 MPa; ηplug = 95.1%	Fractured reservoirs (water control)	Singh [94]
Zwitterionic copolymer reinforcement	Charge neutralization and ion-shielding compensation improve salinity tolerance and hydration stability	Poly(sulfobetaine-co-acrylamide) (P(SB-co-AM)); Poly(carboxybetaine-co-AMPS)	High salinity (>200,000 mg L−1 NaCl), 90–120 °C	High-salinity reservoirs; salt-resistant rheology modifiers	Lowe and McCormick [95]
Pickering emulsion flooding	Amphiphilic copolymer-stabilized anionic emulsion	AMPSA/AA/DMA	94% displacement efficiency	Residual-oil recovery in porous media	Ntente [96]
Supramolecular dynamic network	Hydrophobic and H-bond crosslinking via methacrylate–ammonium system.	Methacrylate/DTAB	135 °C; compressive strength ≈ 14.5 MPa	Cavernous/deep reservoirs	Yang [97]
Active amphiphilic polymer	C16 long-chain hydrophobic association.	AM/AA/C16DMAAC	>95% viscosity reduction at 60 °C	Heavy oil reservoirs (EOR)	Yang [98]
Al2O3 nanoparticle filling	Nanoparticles improve high-T/salinity tolerance and thermal stability	NaSS/DMA/Al2O3/Cr(III)	Stable 300 days at 150 °C	High-T aging reservoirs (plugging)	Pandit [99]

). Despite these advances, current systems still rely predominantly on single-mechanism reinforcement, without establishing a unified structure–performance–environment correlation model. Moreover, the poor degradability of hydrophobic monomers and the agglomeration of nanoparticles at high concentrations continue to impair interfacial compatibility and dispersion stability, posing key challenges for large-scale and sustainable applications.

Building upon the advances in structural reinforcement, recent research has shifted its focus from purely performance-oriented design toward environmentally responsible gel systems. Despite significant improvements in mechanical integrity and HT/HS tolerance, conventional transition-metal crosslinkers—particularly chromium-based species [100,101]—pose persistent ecological and toxicological concerns due to their high mobility and oxidation potential [102,103]. Consequently, the green substitution of crosslinking systems has emerged as a critical pathway to achieve dual objectives: maintaining network strength while mitigating environmental hazards. This section, therefore, discusses the current strategies for green substitution and ecological risk reduction, with emphasis on (i) low-toxicity ligand chelation and (ii) multivalent-metal replacement.

(1)

Low-toxicity ligand chelation. Organic acids bearing carboxyl/hydroxyl groups—such as acetic, propionic, and citric acids—serve as eco-friendly ligands that coordinate metal ions and enable delayed/controlled crosslinking [104]. As shown in Figure 3, their functional groups underpin chelation in gel networks. Notably, citric acid, due to its tri-carboxylic configuration, affords higher complex stability, promotes the reduction of Cr(Ⅵ) into the less toxic Cr(III), and suppresses the leaching/mobility of chromium species, thereby mitigating ecological risk—consistent with Lockhart’s ligand-exchange theory [105]. Representative coordination strengths and thermal stabilities are summarized in

Table 3. Representative organic ligands and their corresponding performance parameters.

Organic Ligand	Structural Features	Chelation Ability	Release Control Ability	Thermal Stability	Representative References
Acetic acid	Monocarboxylic acid; limited coordination sites.	Medium	Medium	Medium	Guanghua Yi and Michael Sayer [106,107], Kang [108]
Propionic acid	Longer chain; slightly stronger chelation.	Medium	Medium	Medium	Mumallah [109], Kaddouri [110]
Boric acid	Trihydroxy structure; stable borate complexation.	Strong	Strong	Strong	Citeseer [111], Shah et al. [112], Brannon [113], Harris [114]
Lactic acid	α-Hydroxy and carboxyl groups; stabilizes Cr(III) complexes.	Strong	Relatively strong	Strong	Yang et al. [82], Jong et al. [115]
Citric acid	Tricarboxylic; multi-site coordination with hydroxyl groups.	Strong	Strong	Strong	Niu et al. [116], Takahashi [117]
Phenolic resin	Polyhydroxy aromatic; forms rigid chelated networks.	Strong	Relatively strong	Relatively strong	Shibayama [118], Ran et al. [119]

.

(2)

Multivalent metal substitution. Replacing toxic chromium with lower-toxicity multivalent cations—e.g., Al(III), Zr(IV), and Ti(IV)—enables controllable gelation and improved thermal endurance. Practical implementations combine delayed-release complexants (e.g., lactate/citrate) with nano-enhancement to sustain long-term integrity under high-temperature/high-salinity conditions. Representative formulations and operating windows are compiled in

Table 4. Representative enhancement strategies, formulations, and application conditions.

Enhancement Strategy	Key Technique/Mechanism	Representative Formulation	Applicable Conditions	Performance Advantages
Hydrophobic association [120,121,122]	Styrene/acrylate monomers form hydrophobic domains	HPAM-St	100–140 °C, high salinity	Improved elasticity and shear resistance.
Host–guest inclusion [123,124,125]	β-CD–adamantane supramolecular complexation	βCD-g-PAM	>90 °C, salinity 1.5 × 105 mg L−1	Controllable density, low-dosage efficiency.
Nanofiller incorporation [126,127,128]	SiO2, montmorillonite, or fly ash reinforcement	HPAM/SiO2	Up to 160 °C	Enhanced thermal strength and plugging.
Organic chelation [129,130,131]	Citric acid–Cr(III) coordination	HPAM-Cr(III)-citric acid	Delayed gelation (24–480 h)	Tunable gelation, reduced toxicity.
Metal substitution [132,133]	Ti(Ⅵ), Al(III), Fe(III) replacing Cr(III)	HPAM–Ti(IV)	Delayed gelation (24–480 h)	Tunable gelation; reduced toxicity

.

Overall, these green-substitution strategies substantially mitigate the ecological risks associated with transition-metal gels while maintaining structural robustness, laying the groundwork for sustainable next-generation conformance-control materials. Nevertheless, system-level gaps persist in gelation precision, service durability, and long-term environmental behavior. To systematically quantify these trade-offs, this study employs a life cycle assessment (LCA) framework (Figure 4) that defines explicit system boundaries to evaluate the carbon footprint and environmental burdens throughout synthesis, service, and end-of-life stages.

To quantitatively evaluate these trade-offs, recent studies have introduced life cycle assessment (LCA) approaches to assess the carbon footprint and environmental burdens of metal-ion gels across their synthesis, service, and end-of-life stages [134,135,136,137,138]. Building upon this foundation, we establish an LCA framework [139,140] with clearly defined system boundaries (Figure 4) to examine whether molecular structure tuning and crosslinking-pathway modification can effectively reduce life cycle carbon emissions and ecological burdens. Within this framework, green alternative systems are treated as candidate solutions, offering potential “dual benefits”—network enhancement and toxicity mitigation—though their industrial scalability still depends on high-throughput formulation screening and interfacial-regulation optimization.

A methodological limitation of this analysis is the reliance on globally parameterized LCIA characterization factors; as region-specific datasets (e.g., the emerging Canada-context LCIA) mature [141], recalculation may refine both the absolute impact magnitudes and the relative ranking of alternatives.

2.1.2. Evolution and Green Transition of Organic Gels

As a complement to metal-based gels, organically crosslinked gels have attracted increasing attention due to their renewable raw materials, low toxicity, and tunable crosslinking mechanisms. Current green strategies mainly rely on natural polysaccharides and chitosan to construct metal-free and biodegradable gel networks. These systems offer distinct advantages in terms of gelation precision and ecological safety, and have been successfully applied in pilot-scale oilfield operations [142,143,144].

However, significant challenges remain in achieving a balance between degradability, structural stability, and service performance, which limits their applicability under harsh reservoir conditions such as high temperature, high salinity, and high pressure [145,146,147]. Most studies still emphasize the enhancement of a single property, while lacking a systematic understanding of the synergies between the “crosslinking chemistry–network architecture–reservoir-coupled environment”. This mismatch between design parameters and in situ service conditions hinders large-scale deployment. The key issues can be summarized as follows:

(1)

Deviation between “green” labels and actual environmental behavior. Current green evaluations of organic gels primarily emphasize “metal-free” or “low-toxicity” labeling while overlooking their real environmental behavior under subsurface conditions. Most degradation assessments are performed under ambient temperature and neutral pH, failing to capture the actual degradation pathways, migration of byproducts, and ecological risks that occur under high-temperature and high-salinity environments. For instance, tannic acid-based crosslinkers, though commonly regarded as eco-friendly, may generate phenolic intermediates during degradation, potentially leading to groundwater contamination and secondary environmental hazards [148,149].

(2)

Trade-off between performance and degradability. Flexible organic gels possess excellent hydrophilicity and biocompatibility but often suffer from insufficient mechanical strength, brittleness, and short service lifetimes under high-temperature and high-pressure conditions [150,151]. For instance, polysaccharide-based gels generally exhibit a loose three-dimensional network and limited toughness due to their high water content [152]. Enhancing thermal stability and plugging efficiency typically requires an increase in crosslinking density or the incorporation of rigid monomers. Although these strategies significantly improve mechanical robustness, they inevitably compromise biodegradability. Conversely, excessive network softening enhances degradability but reduces mechanical endurance, resulting in a clear mismatch between gel strength and degradability (Figure 5a) [153].

(3)

Lack of a “structure–property–environment” model. Current design approaches for organic gels remain largely empirical, lacking predictive frameworks capable of describing the coupling effects among molecular structure, performance, and environmental fate. For example, polyethyleneimine (PEI)-based systems can mitigate heavy-metal contamination; however, their long-term environmental behavior and potential toxicological impacts under reservoir conditions remain poorly understood [154,155,156]. This theoretical gap reflects the absence of an integrated understanding of the “green structure–service performance–environmental adaptability” relationship in the existing literature on organic gels.

(4)

Need for a coupled “structure–performance–degradation” model. To achieve both structural reliability and environmental compatibility, it is essential to develop a coupled “structure–performance–degradation behavior” model under realistic reservoir boundary conditions. Such a model would provide molecular-level guidance for rational gel design, enabling dynamic optimization between mechanical strength and degradability, and ultimately advancing the green and sustainable transformation of organic gel systems (Figure 5b).

Achieving the effective transformation from laboratory “green labels” to field-scale “green applications” requires overcoming multi-scale design barriers spanning the molecular, mesoscopic, and macroscopic levels. To this end, a synergistic regulatory framework that integrates molecular structure–service behavior–environmental fate must be established. In recent years, bio-based crosslinkers (e.g., citric acid, chitosan, and alginate) [157,158,159], multifunctional responsive monomers [160,161], and combined physical–chemical crosslinking strategies [162,163,164] have provided diverse routes for advancing the green transition of gels. Particularly under the constraints of balancing “greenness–performance” synergy, organic gels are rapidly evolving toward intelligent responsiveness, self-healing capabilities, and modular network architectures. A critical step in this process is the construction of a structure–environmental response–service evolution coupling model. This model, built upon the microscopic configuration of gel networks and incorporating dynamic response mechanisms with reservoir boundary conditions, aims to enhance structural reversibility and environmental adaptability, thereby enabling the transition from traditional “passive plugging” to “active adaptation”. Specific strategies include the following:

(1)

Employing dynamic covalent bonds (e.g., Schiff bases and cleavable ester bonds) in synergy with physical association units (e.g., hydrophobic associations and host–guest recognition) to construct reversible networks, thus improving structural reversibility and service stability [71,165,166,167,168,169];

(2)

Integrating multicarboxyl/multihydroxyl ligands with degradable monomers to introduce programmable degradation nodes, enabling precise control over service lifetime and ecological release pathways [170,171,172,173];

(3)

Leveraging environmental stimuli such as temperature, pH, salinity, and shear disturbances to trigger network reconstruction or phase transitions, thereby promoting dynamic coupling between material performance and multi-field evolution (seepage–mechanical–chemical).

This approach enables full-cycle responsive functions—including timely injection, adaptive plugging, and controlled degradation—demonstrating superior functional stability in complex reservoir conditions (structural response mechanisms illustrated in Figure 6) [174,175,176]. Building upon these advancements, the design of double-network (DN) hydrogels has emerged as an effective pathway for enhancing toughness and service lifetime [177,178,179,180,181]. In such systems, the rigid primary network provides structural support, while the flexible secondary chains dissipate energy and buffer stress, collectively improving strength and deformation resistance [182,183,184,185]. Recent studies utilizing high-throughput simulations have revealed the fracture characteristics and degradation pathways of crosslinking nodes (e.g., citric acid and glutaraldehyde) under extreme reservoir conditions [183], thereby offering theoretical guidance for multi-scale controllable design.

In summary, the green transformation and intelligent evolution of organic gels are redefining the design paradigm of oilfield profile control materials. Crosslinking pathways differ systematically in precision, stability, and environmental adaptability, yet all face trade-offs between performance and sustainability. Figure 7 and

Table 5. Comparison of crosslinking mechanisms, technical features, and limitations of three representative organic gel systems.

System Type	Cross Linking Mechanism	Technical Features	Limitations
Small-molecule aldehydes [192,193,194]	Schiff base condensation (–C=N–)	Adjustable gelation time (10–300 min)	Formaldehyde/glutaraldehyde residues (LC50 = 12 mg L−1)
Organic acid esterification [195,196,197,198,199]	Carboxylic ester bonding (–COO–)	pH responsiveness (swelling ratio > 300%)	Ester hydrolysis under high salinity (modulus loss > 60%)
Natural polymer ion-bridging [200,201]	Divalent cation coordination [Ca(II)/Mg(II)]	High biodegradability (>80%, OECD 301B)	Network dissociation at high temperature (>80 °C)

highlight the inherent complexity of achieving a “greenness–performance” synergy. Crucially, the long-term stability of these gels in high-temperature, high-salinity, and heterogeneous reservoirs is not solely determined by crosslinking chemistry but also by dynamic evolution under coupled seepage–mechanical–chemical fields. Understanding such failure mechanisms and their geomechanical coupling thus emerges as a central research direction for advancing the functional stability and environmental safety of organic gels.

2.1.3. Profile Control Failure Driven by Reservoir Dynamic Evolution

The pore structure of heterogeneous reservoirs is not static but undergoes continuous evolution under sustained seepage processes [202,203,204,205]. In low-permeability sandstones, for example, the distribution of interstitial materials and grain size gradation determines the advance of the waterflooding front and the sweep efficiency [206]. Studies indicate that pore structure governs seepage behavior through three key mechanisms:

• Pore network topology dictates the connectivity of dominant flow channels [207,208,209,210];
• Particle size distribution controls interfacial tension and capillary resistance [211,212,213,214,215];
• Cementation type affects wettability evolution and displacement sequence [216,217,218,219].

This mechanism has been validated in the Karamay Oilfield, where preferential plugging of macropores induced synergistic imbibition in micropores, thereby enabling a more efficient regulation pathway [220]. Thin-section casting, XRD, and visualized displacement experiments further confirm a strong negative correlation between microscopic heterogeneity and oil recovery efficiency. This provides a systematic recognition chain for intelligent profile control: problem identification (MHI index) → mechanism analysis (graded mobilization) → responsive plugging (material regulation).

Table 6. Experimental data on the correlation between reservoir pore–microscopic heterogeneity parameters and multi-stage oil displacement efficiency under water flooding dynamics [206].

Model	Pore-Scale Heterogeneity	Microscopic Heterogeneity	Oil Recovery (Water-Free Stage, %)	Final Oil Recovery (%)	Relative Wettability Index	Water-Cut Variation Rate
X1	High	Low	6.3	25	0.17	3.5
X2	Low	High	10.9	31.8	0.47	2.7
X3	Lowest	Lowest	8.5	30.8	0.35	3.8
X4	Highest	Highest	5.0	20.8	0.51	2.9
X5	Medium	Medium	6.9	25	0.18	2.8

summarizes representative experimental parameters, showing that fluid migration is jointly controlled by pore–throat geometry (throat radius and pore-to-throat volume ratio) [221,222,223,224,225,226] and fluid dynamic parameters (capillary-to-viscous force ratio and pressure gradient, ΔP) [227,228,229,230]. When the permeability contrast, Δk, exceeds 5, profile control agents tend to penetrate high-permeability channels while failing to sufficiently imbibe into low-permeability zones, leading to the phenomenon of “blocking without sealing, sealing without effectiveness” [231,232,233,234]. Further analysis indicates that profile control failure is primarily induced by three categories of multi-field coupling mechanisms:

• Seepage–structure disequilibrium: Pore structure reconstruction facilitates agent escape, reducing plugging efficiency;
• Amplified mechanical disturbance: Injection of blocking agents induces local stress concentration, triggering throat rearrangement, skeleton loosening, or dissolution damage [235,236,237,238];
• Seepage diversion enhances bypass flow, increases injection energy consumption, and necessitates excessive chemical dosage, thereby elevating unit oil-production carbon emissions, aggravating oily wastewater burdens, and increasing the risk of formation dissolution and reservoir integrity loss [239,240,241,242,243].

Experimental results demonstrate that pore-scale architecture and microscopic heterogeneity exert a decisive influence on oil displacement efficiency and waterflood dynamics. Models with lower heterogeneity (X2; X3) achieved higher ultimate recovery factors (30–32%) by improving sweep efficiency, whereas the highly heterogeneous model (X4) exhibited a markedly lower recovery of 20.8% due to rapid breakthrough of dominant channels. Relative wettability indices and water-cut variations further revealed that pore-microstructure coupling not only governs the stability of displacement fronts but also directly dictates the spatial distribution of residual oil. These findings highlight that the effectiveness of profile control gels depends not only on their intrinsic material structure but is also strongly constrained by reservoir heterogeneity and its dynamic evolution. Overall, conventional gels, characterized by structural rigidity and weak adaptability, are prone to failure through channeling, penetration, or dispersion under coupled seepage–mechanical–chemical conditions, reflecting a fundamental contradiction between structural design and dynamic adaptability. Moreover, their lack of precise recognition of dominant channels and tunable blocking capacity results in poor utilization efficiency and limited longevity. Consequently, the development of next-generation green gels with intelligent responsiveness, self-adaptive blocking, and environmental compatibility, along with the establishment of systematic classification and evaluation frameworks, has become a key research direction to drive the green transformation and low-carbon development of profile control technologies.

2.2. Green Grading Results

Based on the ETI–CFI framework, four representative gel systems were classified into distinct green performance levels (

Table 7. Quantitative comparison of green performance of different types of polymer gels.

Type	Representative System	ETI Score	CFI Score (kgCO2e/kg)	Classification Validation
Class Ⅰ Conventional	Cr(III)-PAM	1.45	9.1	Meets Class Ⅰ criteria
Class II Low-toxicity	Al-PAM/Zr-PAM	0.68	4.3	Meets Class II criteria
Class III Eco-friendly	Citric acid–chitosan	0.42	2.1	Meets Class III criteria
Class IV Intelligent green	PH-responsive nanogels	0.25	1.2	Meets Class IV criteria

).

• Class I (Conventional): Cr(III)-HPAM, exhibiting strong plugging capacity but associated with high toxicity, poor degradability, and a significant carbon footprint.
• Class II (Low-Toxicity): Al-PAM and Zr-PAM, with reduced toxicity compared to Cr-based gels, yet still carbon-intensive.
• Class III (Eco-Friendly): Citric acid–chitosan gels, derived from renewable raw materials, biodegradable, and low in toxicity.
• Class IV (Intelligent Green): pH-responsive nanogels, integrating environmental adaptability, degradability, and functional responsiveness with a low-carbon synthesis pathway.

2.3. Case Studies

To validate the applicability of the ETI-CFI green grading framework, four representative gel systems were selected for quantitative comparison. Following the dual-indicator methodology outlined in

Table 8. Classification criteria for green polymer gels (developed based on REACH, EPA, ISO 14067, and PlasticsEurope databases).

Level	Definition	ETI Value	CFI (kgCO2e/kg)	Typical Features	Representative Systems
I. Conventional	High efficiency; high toxicity and carbon footprint.	>1.2	>8	Non-degradable; Cr(III) or aldehyde/phenolic crosslinkers.	Cr(III)-HPAM, glutaraldehyde–PAM gels
II. Low-toxicity	Lower toxicity; moderate carbon intensity.	0.5–0.8	3–8	Partial replacement of toxic ions or organics.	Al–PAM, Zr–HPAM, organic acid–PAM gels
III. Eco-friendly	Renewable and partially degradable networks.	0.3–0.5	1.5–3	Biopolymer matrix; low-toxicity organic crosslinkers.	Citric acid–chitosan, alginate, scleroglucan gels
IV. Intelligent green	Responsive, degradable, low-carbon systems.	<0.3	<1.5	Stimuli-responsive, self-healing, bio-based dual networks.	pH-responsive nanogels, double-network gels

and

Table 9. Evaluation framework for Environmental Toxicity Index (ETI).

Evaluation Dimension	Weight (Suggested)	Scoring Criteria (Example)	Score Range
Chemical Toxicity	0.4	LD50/LC50 < 50 mg/kg→ high score; higher LD50/LC50 → lower score	0~1
Ecological Persistence and Bioaccumulation	0.2	Persistent (P) + Bioaccumulative (B) → maximum score; no bioaccumulation → minimum score	0~1
Degradability	0.3	Completely degradable → minimum score; non-degradable → maximum score	0~1
Regulatory Concern	0.1	Listed in REACH SVHC or EPA priority list → maximum score	0~1

, both the Environmental Toxicity Index (ETI) and Carbon Footprint Intensity (CFI) were systematically evaluated. The comparative results are summarized in

Table 10. Evaluation framework for Carbon Footprint Intensity (CFI).

Evaluation Dimension	Weight (Suggested)	Scoring Criteria (Example)	Score Range
Raw material carbon footprint	0.5	Based on LCA database values, kg (CO2e kg−1)	0–1
Energy consumption during synthesis	0.2	High-temperature/high-pressure synthesis = high score; ambient-temperature synthesis = low score	0–1
Energy demand during application	0.2	High injection energy or difficulty = high score	0–1
Post-treatment carbon emissions	0.1	Incineration/landfilling = high score; biodegradable/recyclable = low score	0–1

. To provide further insight, two systems are presented as illustrative cases.

• Example A. Cr(III)-HPAM Gel (Conventional, Class I)
• A1. ETI Calculation (weights: toxicity 0.4, persistence 0.2, degradability 0.3, regulation 0.1)

• Chemical toxicity, S₁ = 0.85: Cr compounds exhibit significant acute and chronic toxicity, while residual acrylamide monomers pose occupational hazards (data source: ECHA/EPA databases);
• Environmental persistence, S₂ = 0.70: Cr is classified as environmentally persistent with bioaccumulation concerns;
• Biodegradability, S₃ = 1.00: HPAM networks are non-biodegradable;
• Regulatory concern, S₄ = 1.00: Cr salts are widely listed as substances of very high concern (SVHC) or subject to strict EPA regulation.

$$ETI=2\times \sum _{i=1}^4(S_i\times W_i)=2\times (0.85\times 0.4+0.70\times 0.2+1\times 0.3+1\times 0.1)=1.45$$

(1)

• A2. CFI Calculation (weights: raw materials 0.5, synthesis 0.2, use 0.2, disposal 0.1)

• Raw material carbon footprint, $C_{\mathit{raw}}=10\mathrm{kg}{\mathrm{CO}}_2\mathrm{e}{\mathrm{k}\mathrm{g}}^{-1}$: The baseline HPAM footprint ($5–7\mathrm{kg}{\mathrm{CO}}_2\mathrm{e}/\mathrm{kg}$) was conservatively upscaled to $10\mathrm{kg}{\mathrm{CO}}_2\mathrm{e}/\mathrm{kg}$ by allocating upstream contributions from Cr-salt production and chelation co-reagents, as well as packaging and transportation. This upscaling was based on existing research and LCA data from reputable sources, including the Ecoinvent and ELCD databases, which provide comprehensive life cycle assessments of similar chemicals and their associated environmental impacts [244,245,246].
• Synthesis energy demand, S₂ = 0.80: Polymerization and post-processing steps are energy-intensive.
• Operational energy demand, S₃ = 0.75: High injection pressure and polymer dosage increase energy input.
• Disposal impact, S₄ = 1.00: Disposal mainly relies on incineration or landfilling with limited valorization pathways.

$$CFI=C_{raw}\times \left[\sum _{j=2}^4{(S}_j\times W_j)+W_1\right]=10\times \left[(0.80\times 0.2+0.75\times 0.2+1\times 0.1)+0.5\right]=9.1\mathrm{k}\mathrm{g}{\mathrm{CO}}_2\mathrm{e}{\mathrm{k}\mathrm{g}}^{-1}$$

(2)

• Classification: ETI = 1.45, CFI = 9.1 ⇒ Class Ⅰ (Conventional).
• Interpretation: This system combines effective plugging performance with substantial environmental burdens. The high ETI reflects acute/chronic toxicity, non-biodegradability, and stringent regulatory restrictions. The elevated CFI is driven by raw-material carbon intensity, energy-intensive synthesis, and limited end-of-life valorization. Overall, it constitutes a “performance-oriented but environmentally unsustainable” gel system.
• Example B. Al–PAM/Zr–PAM Gel (Low-Toxicity; Class II)
• B1. ETI Calculation

• Chemical toxicity, S₁ = 0.30: Al/Zr salts are far less acutely toxic compared with Cr.
• Environmental persistence, S₂ = 0.20: Low persistence and bioaccumulation concern.
• Biodegradability, S₃ = 0.55: PAM backbone remains largely non-degradable.
• Regulatory concern, S₄ = 0.15: Generally subject to standard regulatory oversight.

$$ETI=2\times \sum _{i=1}^4{(S}_i\times W_i)=2\times (0.30\times 0.4+0.20\times 0.2+0.55\times 0.3+0.15\times 0.1)=0.68$$

(3)

• B2. CFI Calculation

• Raw material carbon footprint, $C_{\mathit{raw}}=5.4\mathrm{k}\mathrm{g}{\mathrm{CO}}_2\mathrm{e}{\mathrm{k}\mathrm{g}}^{-1}$: Lower-range PAM values with moderate adjustment for Al/Zr salt preparation.
• Synthesis energy demand, S₂ = 0.60: Partly requires high-temperature and aqueous processing.
• Operational energy demand, S₃ = 0.60: Moderate injection pressure and dosage.
• Disposal impact, S₄ = 0.50: Mainly landfilling with partial valorization options.

$$CFI=C_{raw}\times \left[\sum _{j=2}^4{(S}_j\times W_j)+W_1\right]=5.4\times \left[(0.60\times 0.2+0.60\times 0.2+0.50\times 0.1)+0.5\right]=4.3\mathrm{k}\mathrm{g}{\mathrm{CO}}_2\mathrm{e}{\mathrm{k}\mathrm{g}}^{-1}$$

(4)

• Classification: ETI = 0.68, CFI = 4.3 ⇒ Class II (Low-Toxicity System).
• Interpretation: This system demonstrates reduced toxicological risks compared with Cr-based gels, as reflected in its lower ETI score. The substitution of Al/Zr crosslinkers decreases acute toxicity and ecological persistence, yet the use of a non-biodegradable PAM backbone remains a limiting factor. The moderate CFI arises from raw-material requirements, partially energy-intensive synthesis, and disposal routes dominated by landfilling. Overall, it represents a “toxicity-mitigated but still carbon-intensive” gel system, suitable as a transitional alternative but not fully aligned with long-term sustainability targets.
• Example C. Citric Acid-Chitosan Gel (Eco-Friendly, Class III)
• C1. ETI Calculation

• Chemical toxicity, S₁ = 0.20: Citric acid and chitosan are considered non-toxic with low occupational exposure risks.
• Environmental persistence, S₂ = 0.10: Not classified as persistent or bioaccumulative.
• Biodegradability, S₃ = 0.10: Chitosan is biodegradable, and the crosslinked network retains degradability.
• Regulatory concern, S₄ = 0.10: Neither component is listed under SVHC or EPA high-priority categories.

$$ETI=2\times \sum _{i=1}^4(S_i\times W_i)=2\times (0.20\times 0.4+0.10\times 0.2+0.10\times 0.3+0.1\times 0.1)=0.42$$

(5)

• C2. CFI Calculation

• Raw material carbon footprint, $C_{\mathit{raw}}=2.2\mathrm{k}\mathrm{g}{\mathrm{CO}}_2\mathrm{e}{\mathrm{k}\mathrm{g}}^{-1}$: Bio-based chitosan and citric acid; mid-range values from LCA databases and the literature.
• Synthesis energy demand, S₂ = 0.20: Gel formation occurs in mild aqueous conditions at room temperature.
• Operational energy demand, S₃ = 0.30: Relatively low injection pressure and polymer concentration.
• Disposal impact, S₄ = 0.10: Biodegradable with potential for resource recovery.

$$CFI=C_{raw}\times \left[\sum _{j=2}^4{(S}_j\times W_j)+W_1\right]=2.2\times \left[(0.20\times 0.2+0.30\times 0.2+0.10\times 0.1)+0.5\right]=2.1\mathrm{k}\mathrm{g}{\mathrm{CO}}_2\mathrm{e}{\mathrm{k}\mathrm{g}}^{-1}$$

(6)

• Classification: ETI = 0.42, CFI = 2.1 ⇒ Class III (Eco-Friendly).
• Interpretation: This system exemplifies the benefits of bio-based feedstocks and degradable crosslinkers. The low ETI reflects the non-toxic nature and biodegradability of citric acid and chitosan, as well as their exclusion from major regulatory concern lists. The moderate CFI results from renewable raw materials, mild aqueous synthesis under ambient conditions, relatively low operational energy demands, and environmentally benign disposal pathways. Overall, it constitutes a “biodegradable and carbon-mitigated” gel system, highlighting its strong potential for sustainable oilfield applications.
• Example D. pH-responsive nanogels (Intelligent green; Class IV)
• D1. ETI Calculation

• Chemical toxicity, S₁ = 0.10: Derived from natural or bio-based monomers, inherently low toxicity.
• Environmental persistence, S₂ = 0.10: Low persistence with reversible hydration/dehydration.
• Biodegradability, S₃ = 0.20: Introduction of hydrolysable/cleavable linkages enables partial degradability.
• Regulatory concern, S₄ = 0.05: No inclusion of SVHC or high-priority substances.

$$ETI=2\times \sum _{i=1}^4(S_i\times W_i)=2\times (0.10\times 0.4+0.10\times 0.2+0.20\times 0.3+0.05\times 0.1)=0.25$$

(7)

• D2. CFI Calculation

• Raw material carbon footprint, $C_{\mathit{raw}}=2.0\mathrm{k}\mathrm{g}{\mathrm{CO}}_2\mathrm{e}{\mathrm{k}\mathrm{g}}^{-1}$: Bio-based feedstocks or low-carbon synthetic precursors.
• Synthesis energy demand, S₂ = 0.15: Typically prepared under mild aqueous conditions.
• Operational energy demand, S₃ = 0.25: Low concentration and injection pressure, with potential for self-adaptive swelling/plugging.
• Disposal impact, S₄ = 0.10: Biodegradable with potential for recycling or valorization.

$$CFI=C_{raw}\times \left[\sum _{j=2}^4{(S}_j\times W_j)+W_1\right]=2.2\times \left[(0.15\times 0.2+0.25\times 0.2+0.10\times 0.1)+0.5\right]\approx 1.2\mathrm{k}\mathrm{g}{\mathrm{CO}}_2\mathrm{e}{\mathrm{k}\mathrm{g}}^{-1}$$

(8)

• Classification: ETI = 0.25, CFI = 1.2 ⇒ Class IV (Intelligent Green System)
• Interpretation: This system integrates environmental friendliness with functional responsiveness. The very low ETI reflects the use of benign, bio-derived precursors and the absence of major toxicological or regulatory concerns. The low CFI is due to natural raw materials, mild synthesis conditions, minimal injection energy requirements, and fully degradable or recyclable end-of-life pathways. In addition, the pH-responsive network provides controllable plugging and adaptive behavior under reservoir conditions. Overall, it represents a “functionally adaptive and environmentally sustainable” gel system, aligning closely with long-term low-carbon and green development goals.

2.4. Framework Validation

The case studies demonstrate that the ETI–CFI framework can systematically differentiate polymer gels; beyond this, it also reveals underlying coupling laws between material properties and environmental performance. First, toxicity and carbon intensity are often correlated but not strictly proportional, as reflected in Al/Zr–PAM systems, which exhibit moderate ETI values but relatively high CFI due to energy- and carbon-intensive synthesis. Second, degradability provides dual benefits by reducing both ETI scores, through lower ecological persistence, and CFI scores, through reduced disposal-related carbon load. Third, intelligent responsiveness enhances adaptability under harsh reservoir conditions while simultaneously lowering operational energy demand, thereby decreasing both indicators.

Collectively, these findings validate the ETI–CFI framework not merely as a classification tool but also as a method capable of capturing performance–environment trade-offs and synergy effects. This methodological robustness strengthens the connection between laboratory-scale evaluation and life cycle environmental assessment, providing a reliable foundation for guiding further research and application.

2.5. Engineering and Academic Implications

The ETI–CFI dual-indicator framework not only validates the differentiation of polymer gels across toxicity and carbon intensity dimensions but also provides practical implications for both engineering practice and academic research. From an engineering perspective, it offers a quantitative tool to screen and select green gels prior to field deployment, ensuring that candidate systems satisfy both performance requirements and environmental constraints. This approach reduces trial-and-error in pilot tests, optimizes injection strategies, and minimizes the ecological footprint of chemical profile control operations. From an academic standpoint, the framework establishes a standardized methodology that links molecular-level design with life cycle environmental performance, thereby supporting the rational development of low-toxicity ligands, renewable feedstocks, and degradable polymer networks. Moreover, the construction of open-access ETI–CFI databases can facilitate cross-comparison among gel systems, provide benchmark data for multi-scale modeling, and serve as a foundation for AI-assisted predictive platforms.

To summarize, the proposed framework carries four key implications:

• Quantitative benchmark: Establishes a standardized and transparent metric for evaluating environmental performance across gel systems.
• Design guideline: Provides a systematic reference for reconciling plugging efficiency and structural stability with degradability and environmental compatibility.
• Screening tool: Serves as a practical method for identifying suitable gel systems under dual-carbon policy constraints and environmental regulations.
• Theoretical foundation: Links material innovation with low-carbon development strategies, offering a scientific basis for sustainable substitution and performance optimization in profile control applications.

Collectively, these implications underscore the framework’s value not only as a scientific evaluation tool but also as a strategic guide for advancing polymer gels toward intelligent, low-carbon, and environmentally sustainable oilfield applications.

3. Conclusions

This study established and validated a dual-indicator framework combining the Environmental Toxicity Index (ETI) and Carbon Footprint Intensity (CFI) for quantitative evaluation of polymer gels. The results show that Cr(III)-HPAM, while effective in plugging, bears the heaviest environmental burden; citric acid–chitosan systems markedly reduce toxicity and carbon emissions; and pH-responsive gels achieve a balance between stability and sustainability. Key challenges remain, including reconciling strength with degradability, accounting for dynamic reservoir conditions, and addressing the lack of open ETI–CFI databases. Future work should emphasize multi-scale modeling, reconfigurable gel networks, and AI-assisted predictive platforms to accelerate the transition toward intelligent, low-carbon, and environmentally sustainable oilfield applications.

4. Materials and Methods

This section details the proposed ETI–CFI framework, including its theoretical basis and calculation logic for evaluating the environmental performance of polymer gels.

4.1. Green Grading Framework

The classification of polymer gels was developed using a dual-indicator framework that integrates the Environmental Toxicity Index (ETI) and Carbon Footprint Intensity (CFI). This framework enables systematic comparison and hierarchical classification of gel systems into four categories—conventional, low-toxicity, eco-friendly, and intelligent green—based on toxicological attributes, life cycle carbon emissions, degradability, and environmental adaptability (Figure 8). It provides a quantitative basis for assessing green material performance and guiding sustainable formulation design.

Data Sources and Theoretical Basis [247,248,249,250]

The classification criteria and corresponding threshold ranges presented in Table 8 were not derived from new experimental measurements but were formulated through a systematic integration and critical analysis of existing research, publicly available databases, and international regulatory documents. This approach ensures that the proposed evaluation system is grounded in established environmental benchmarks and consistent with international sustainability assessment standards. Specifically, the toxicological indicators were sourced from the European Union’s REACH Substances of Very High Concern (SVHC) list and the U.S. Environmental Protection Agency (EPA) Priority Substance database, while the carbon footprint boundaries were determined in accordance with life cycle assessment (LCA) standards such as ISO 14067 and the PlasticsEurope eco-profile database. Together, these datasets provide a coherent and comparable foundation for constructing a quantitative scoring framework to evaluate the sustainability of polymer gels based on consolidated knowledge from previous studies.

Evaluation Procedure

As shown in Figure 8, the evaluation process consists of three sequential stages:

• Toxicological and Regulatory Screening (ETI): The intrinsic environmental hazards of gel components are assessed through acute and chronic toxicity (e.g., LD₅₀, LC₅₀, NOEC), ecological persistence and bioaccumulation, biodegradability, and regulatory concern (e.g., inclusion in REACH SVHC or EPA priority lists). These indicators are aggregated to yield the ETI, which reflects the overall toxicological risk.
• Carbon Footprint Accounting (CFI): Using life cycle assessment (LCA), greenhouse gas emissions associated with raw material acquisition, synthesis, application, and end-of-life treatment are quantified. The results are expressed as standardized emissions per unit mass of gel (kg CO₂e kg⁻¹), thereby capturing both material-specific burdens and process-related energy inputs.
• Comprehensive Grading and Classification: ETI and CFI values are jointly mapped onto a dual-axis evaluation framework. This enables systematic comparison of different gel systems and classification into distinct green performance levels, ranging from traditional heavy-metal gels to organic crosslinked systems, bio-based degradable gels, and intelligent green gels.

• Threshold Definition and Classification Criteria

The ETI–CFI ranges listed in Table 8 were determined through comparative analysis of representative gel systems, integrating toxicological and carbon-emission data from the aforementioned sources. ETI values were normalized between 0 and 2 according to toxicological weightings, while CFI values were derived from standardized CO₂-equivalent emissions per unit mass of gel.

The threshold intervals correspond to the quartile distribution of existing polymer gel datasets, reflecting a progressive enhancement in environmental performance—from conventional Cr(III)–PAM gels (ETI > 1.2; CFI > 8) to intelligent green gels (ETI < 0.3; CFI < 1.5).

This quantitative–qualitative coupling enables the ETI–CFI framework to serve as a unified criterion for evaluating the sustainability of polymer gel systems in oilfield applications.

4.2. Environmental Friendliness Evaluation

4.2.1. Calculation Logic of Environmental Toxicity Index (ETI)

The ETI quantifies the environmental risk of gel systems by integrating four dimensions:

• Chemical toxicity: Acute and chronic toxicity indicators such as LD50/LC50 and NOEC, typically obtained from authoritative databases (e.g., ECHA, EPA) [251,252,253,254];
• Ecological persistence and bioaccumulation: Classification of persistent (P), bioaccumulative (B), very persistent (vP), or very bioaccumulative (vB) substances [255,256,257,258,259];
• Degradability: Chemical and biological degradability, based on standardized OECD 301B tests (e.g., chitosan is degradable, while HPAM is non-degradable);
• Regulatory concern: whether the material is listed as a Substance of Very High Concern (SVHC) under REACH or on the EPA priority control list [260,261,262,263].

Each indicator was normalized to a 0–1 scale and assigned weights of 0.4, 0.2, 0.3, and 0.1, respectively. The final ETI value was calculated through weighted summation and normalized to the 0–2 range to enhance differentiation. A higher ETI indicates stronger environmental toxicity, and the detailed scoring criteria are summarized in Table 9. The calculation formula is as follows:

$$ETI=2\times {\sum }_{i=1}^4(S_i\times W_i),$$

(9)

where $S_i$ is the normalized score of the i-th indicator, and $W_i$ is the assigned weight. A multiplication factor of 2 is introduced to expand the ETI value into the 0–2 range, thereby improving differentiation between materials of varying environmental risks.

For example, Cr(III)-HPAM gels exhibit high toxicity and poor degradability, contain heavy metals, and are listed on regulatory concern lists—thus yielding a relatively high score. In contrast, citric acid–chitosan gels originate from natural sources, are low in toxicity, biodegradable, and free from regulatory restrictions—resulting in a relatively low score. Overall, a higher ETI value indicates stronger environmental toxicity.

a. Normalization and Weight Determination.

All indicators in the ETI framework were normalized using a min–max linear transformation to a 0–1 scale according to Equation (10):

$$S_i={\displaystyle \frac{X_i-X_{min}}{X_{max}-X_{min}}}$$

(10)

where $X_i$ represents the measured or reported value of each indicator, and $X_{min}/X_{max}$ denote the minimum and maximum reference values obtained from authoritative databases such as ECHA and EPA. The selection of these boundary values followed the recommended ranges for acute toxicity (LD₅₀/LC₅₀), degradability, persistence, and bioaccumulation parameters provided in regulatory or OECD testing guidelines. Higher toxicity, lower degradability, or greater persistence correspond to higher normalized scores.

The weighting factors (Wi) were determined through a modified Delphi approach that evaluates the relative importance of each environmental dimension, using a modified Delphi approach and supported by previous LCA-based ecotoxicity weighting studies [251].

The Delphi process consisted of two iterative consultation rounds involving five experts in polymer chemistry, environmental toxicology, and petroleum engineering. In each round, experts independently ranked the importance of toxicity, persistence/bioaccumulation, degradability, and regulatory concern on a 0–10 scale. The mean and interquartile range (IQR) of expert scores were analyzed to assess consensus; indicators with an IQR ≤ 2 were considered stable. Final weights were obtained by averaging the second-round consensus values and normalizing their sum to unity, resulting in the final weighting vector (0.4, 0.2, 0.3, 0.1).

To ensure future reproducibility and facilitate large-scale application, the weighting matrix ($W_i$) and normalized indicator scores ($S_i$) can be systematically parameterized as input variables for a dedicated ETI–CFI computational module. The normalization standards for each indicator are derived from regulatory and database-defined reference ranges—specifically, OECD testing guidelines and values reported in ECHA and EPA datasets—ensuring consistent interpretation of toxicity, degradability, and persistence indicators across different materials. The weighting scheme is determined using the expert-consensus results obtained from the modified Delphi process, in which the mean and IQR of expert ratings define the relative contribution of each dimension to the overall ETI value. This module can be developed in Python version 3.9.24, Excel, or MATLAB R2020a environments to automatically compute ETI and CFI values through matrix operations based on Equations (9) and (10). The proposed algorithm would read raw indicator data, perform min–max normalization, apply expert-derived weights, and output aggregated scores for each gel system. Such a digital implementation framework provides a transparent and standardized calculation procedure, ensuring reproducibility of normalization and weighting across studies. Moreover, it supports sensitivity and Monte Carlo simulations to quantify uncertainty propagation within the ETI–CFI evaluation process and lays the foundation for integration into future decision-support or life cycle assessment (LCA) software platforms for green-material evaluation. In summary, these procedures establish explicit standards for the selection and calculation of normalized indicator scores and weighting factors in the ETI framework, ensuring methodological transparency and reproducibility.

4.2.2. Carbon Footprint Intensity (CFI) Calculation Logic

The Carbon Footprint Intensity (CFI) quantifies the greenhouse gas emissions associated with the full life cycle of polymer gels. The evaluation includes four stages:

• Raw material footprint (kg CO₂e kg⁻¹): Data are obtained from established life cycle assessment (LCA) databases—such as Ecoinvent (https://ecoinvent.org), GaBi LCA (https://sphera.com/gabi/, accessed on 11 October 2025), and ELCD (https://eplca.jrc.ec.europa.eu/, accessed on 11 October 2025)—or derived from reports from the literature. For reference, typical values include HPAM ≈ 5–7 kg CO₂e kg⁻¹ and chitosan ≈ 2 kg CO₂e kg⁻¹ [264].
• Synthesis energy consumption: Determined by whether high-temperature/high-pressure reactions are required (e.g., HPAM synthesis involves energy-intensive conditions, while chitosan–citric acid gels can be formed under ambient conditions).
• Operational energy consumption: Accounts for injection pressure and concentration requirements during field application (polymer-based systems typically require higher injection pressure).
• End-of-life treatment: Considers degradability and potential for resource recovery (e.g., chitosan gels are biodegradable, whereas Cr-based gels present significant disposal challenges).

Except for the raw material footprint (Stage 1), which is obtained from experimental measurements or LCA databases, the remaining stages (Stages 2–4) are normalized to a 0–1 scale to capture relative differences. The assigned weights for the four stages are 0.5, 0.2, 0.2, and 0.1, respectively. A higher CFI indicates greater carbon emission intensity, and the detailed scoring framework is summarized in Table 10. The final calculation formula is expressed as follows:

$$CFI=C_{raw}\times \left[\sum _{j=2}^4{(S}_j\times W_j)+W_1\right],$$

(11)

where $C_{raw}$ represents the raw material carbon footprint (kg CO₂e kg⁻¹) obtained from LCA databases, $S_j$ denotes the stage-specific scores, and $W_j$ indicates the assigned weight for each stage.

For example, the Cr(III)–HPAM system exhibits a high carbon footprint due to large raw material emissions, high synthesis energy demand, significant injection pressure, and disposal dominated by incineration or landfilling, leading to a high CFI value. In contrast, pH-responsive gels are based on natural raw materials, synthesized under ambient conditions, and partially biodegradable, thus resulting in a relatively low CFI value. In general, a higher CFI corresponds to greater carbon emission intensity throughout the gel life cycle.

b. Normalization and Data Sources.

For CFI evaluation, life cycle data for each stage were collected from Ecoinvent v3.10, GaBi 2023, ELCD 3.2, and peer-reviewed literature. Non-dimensional scores ($S_j$) were derived using min–max normalization across representative gel systems (HPAM, chitosan, alginate, polysaccharide derivatives). Weighting factors ($W_j$) were optimized through sensitivity analysis, ensuring that the dominant contributor—raw material footprint—accounts for approximately 50% of total CFI, consistent with LCA best practices. The normalized and weighted data were then aggregated according to Equation (10) to obtain the composite CFI. Accordingly, the adopted normalization and weighting criteria establish unified and transparent standards for the selection and calculation of all indicators within the CFI framework, ensuring comparability and reproducibility across different polymer gel systems.