Massive carbon dioxide (CO₂) emissions drive climate change. Injecting CO₂ into unconventional reservoirs achieves both enhanced oil recovery (EOR) and geological sequestration. However, simultaneously optimizing oil exchange ratio, CO₂ storage, and net present value remains challenging. This study develops an integrated machine learning (ML)-based framework for multi-objective optimization of CO₂-EOR. A high-resolution reservoir simulation was constructed from field data, and Latin hypercube sampling generated diverse scenarios for proxy training. Mantel's test quantified correlations between input parameters and performance metrics, showing that injection strategy strongly controls net present value, whereas geological properties dominate CO₂ storage. Three ML models—random forest (RF), support vector regression, and artificial neural networks—were evaluated, with RF selected for its superior performance on small datasets. RF was embedded into an improved non-dominated sorting genetic algorithm Ⅱ, enhanced with grey difference degree, crowding distance, and adaptive differential evolution to improve diversity and efficiency. Finally, the technique for order preference by similarity to ideal solution ranked Pareto-optimal solutions through integrating oil productivity, storage, and economics. The proposed framework operationalizes simultaneous high-efficiency tight oil recovery and field-scale CO₂ geological storage, delivering quantitative design rules that embed low-carbon practice into upstream operations and advance the energy sector's greener and sustainable transition.

Nanopores are widely distributed in shale oil formations, and the distinctions induced by nanoconfinement significantly affect the accuracy of multiphase fluid behavior prediction and flow characterization during CCUS-EOR process. While it has been established that fluid-wall interactions, fluid adsorption and capillary pressure strongly influence the CO₂-oil physical properties, most existing studies only focus on a limited combination of such factors. Particularly, wettability is frequently overlooked in the majority of investigations, leading to substantial deviations from true phase behavior and limiting model reliability. Addressing the limitations in current works, we introduce a new critical property shift model coupled with an equilibrium calculation framework based on the modified Peng-Robinson equation of state (PR-EOS) to comprehensively account for these effects. Our results demonstrate that critical properties including saturation pressure, density, and minimum miscible pressure (MMP) decrease as pore size reduces below 75 nm, and gradually approach bulk values for larger pores. Concurrently, capillary pressure increases with the gas-liquid contact angle, enhancing liquid-phase enrichment of light components and consequently decreasing density and saturation pressure. Wettability further alters phase envelope by lowering bubble and upper dew point pressures, raising the lower dew point, and narrowing the two-phase region, while the critical point and MMP remain unchanged. These findings deepen the understanding of nanoconfined fluid flow behavior and provide fundamental knowledge for advancing CCUS-EOR in shale.

High salinity and high oil content present major challenges to the effectiveness of foam in enhanced oil recovery (EOR). This study introduces RCS, a novel oil-resistant foam system designed for reservoirs with salinity levels reaching 2.1 × 10⁵ mg/L. RCS forms stable foams at oil–water ratios up to 60% and is effective across a wide crude oil viscosity range (10.8–7890 mPa·s). We investigated the film properties of oil-containing foam and the co-permeation behavior of the crude oil–N₂–foam system to elucidate the mechanisms underlying foam stability and steady-state flow. RCS emulsified high-viscosity crude oil into stable, large droplets that accumulated within the plateau borders, reducing drainage. Even at concentrations as low as 0.01 wt%, RCS formed stable pseudoemulsion films that prevented intrusion into the gas–water interface, allowing the foam half-life to be mainly controlled by the dilatational viscoelasticity of the interface. With increasing oil–water ratios, both drainage resistance and dilatational modulus increased, extending the drainage and foam half-lives. Coreflood experiments showed that co-injection of RCS with N₂ and crude oil produced stable foams and in-situ emulsions. At 5% oil fractional flow, the critical foam quality (f_g^*) remained unchanged compared to oil-free conditions, although the maximum apparent viscosity decreased by 29.8%. At 10% oil fractional flow, f_g^* shifted to a lower value, while the apparent viscosity in the low-quality regime increased markedly—exceeding that of the oil-free condition. These findings highlight that while crude oil more strongly impairs foam stability in porous media than in bulk, the formation of in-situ emulsions can partially offset or even enhance mobility control through a synergistic Jamin effect. Therefore, in-situ emulsification should be emphasized in foam applications within oil-containing environments.

To meet the growing energy demand and ensure national energy security, improving the recovery rate of developed oil fields and tapping into their remaining oil potential have become important ways to stabilize crude oil production. Given the constraints posed by the intricate nature of reservoir formation conditions and the properties of crude oil, including high viscosity, significant heterogeneity, and low permeability, certain techniques find it challenging to be effectively utilized. In view of this, this article introduces enhance heavy oil recovery by in-situ generated foamy oil, foam flooding in deep fractured vuggy reservoirs, and a new CO₂ responsive fracturing foam fluid, respectively. These results can provide constructive conclusions and suggestions for the study of theories and methods of enhanced oil recovery by gas and foam injection in complex reservoirs.

CO₂ huff-n-puff shows great potential to promote shale oil recovery after primary depletion. However, the extracting process of shale oil residing in different types of pores induced by the injected CO₂ remains unclear. Moreover, how to saturate shale core samples with oil is still an experimental challenge, and needs a recommended procedure. These issues significantly impede probing CO₂ huff-n-puff in extracting shale oil as a means of enhanced oil recovery (EOR) processes. In this paper, the oil saturation process of shale core samples and their CO₂ extraction response with respect to pore types were investigated using online T₁–T₂ nuclear magnetic resonance (NMR) spectroscopy. The results indicated that the oil saturation of shale core samples rapidly increased in the first 16 days under the conditions of 60 ℃ and 30 MPa and then tended to plateau. The maximum oil saturation could reach 46.2% after a vacuum and pressurization duration of 20 days. After saturation, three distinct regions were identified on the T₁–T₂ NMR spectra of the shale core samples, corresponding to kerogen, organic pores (OPs), and inorganic pores (IPs), respectively. The oil trapped in IPs was the primary target for CO₂ huff-n-puff in shale with a maximum cumulative oil recovery (COR) of 70% original oil in place (OOIP) after three cycles, while the oil trapped in OPs and kerogen presented challenges for extraction (COR < 24.2% OOIP in OPs and almost none for kerogen). CO₂ preferentially extracted the accessible oil trapped in large IPs, while due to the tiny pores and strong affinity of oil-wet walls, the oil saturated in OPs mainly existed in an adsorbed state, leading to an insignificant COR. Furthermore, COR demonstrated a linear increasing tendency with soaking pressure, even when the pressure noticeably exceeded the minimum miscible pressure, implying that the formation of a miscible phase between CO₂ and oil was not the primary drive for CO₂ huff-n-puff in shale.

The precise determination of minimum miscible pressure is of great importance for CO₂ enhanced oil recovery and storage as it directly influences the efficiency of pore-scale oil displacement and CO₂ trapping. In this study, an interpretable machine learning framework is developed, enabling the reliable evaluation of nano-confined minimum miscible pressure. Four machine learning algorithms (Random Forest, Multi-layer Perceptron, Support Vector Regression, and eXtreme Gradient Boosting) are employed to accurately predict the nano-confined minimum miscible pressure of a CO₂-oil system. The results demonstrate that, excluding support vector regression, the determination coefficients for all models surpass 94%, signifying the robust predictive performance of our model. Subsequently, Shapley Additive exPlanations is used to analyze the feature importance ranking and the impact of each input feature on minimum miscible pressure in these models. Based on the interpretation results, our multi-layer perceptron model is superior in mining the input-output relationship and reflecting the petrophysical laws, rendering it highly suitable for predicting the minimum miscible pressure while considering nano-confinement. In addition, it is found that pore size significantly influences minimum miscible pressure prediction and that minimum miscible pressure decreases with decreasing pore size when the pore size is ≤75 nm. Single-factor sensitivity analysis is applied to validate the trend patterns between input features and minimum miscible pressure in the multi-layer perceptron model.

Enhanced oil recovery draws increasingly interests from the research and development phases to oilfield implementation worldwide. Due to the complexity of the developed reservoirs and requirement of carbon footprint reduction, new innovations are urgently needed to increase enhanced oil recovery efficiency and/or reduce emissions simultaneously. This paper presents the strategies to improve the enhanced oil recovery performance of carbon dioxide flooding, polymer flooding and imbibition in complex reservoirs. Field trials conducted at Mahu reservoirs demonstrated the potential of nanoemulsion imbibition in stimulating tight oil recovery. These results can provide constructive envision for the development and application of enhanced oil recovery technologies for challenging systems.

Despite the promising results obtained from the utilization of interfacial-active additives in enhancing imbibition-based oil recovery from tight reservoirs, the predominant mechanisms governing this process remain inadequately understood. In this work, a meticulously designed workflow is implemented to conduct experiments and modeling focusing on imbibition tests performed on tight sandstone cores while utilizing surfactant and microemulsion. Our primary objective is to investigate the response of oil recovery to these additives and to develop a robust and reliable model that incorporates the intricate interactions, thereby elucidating the underlying mechanisms. Two imbibition fluids are designed, namely, surfactant and microemulsion. A comprehensive investigation is performed to analyze the physicochemical properties of these fluids, encompassing phase behavior, density, viscosity, and wettability alteration, with the aim of establishing fundamental knowledge in the field. Three imbibition tests are carried out to observe the response of oil production and optimize the experimental methodology. A numerical model is developed that fully couples the evolution of relative permeability and capillary pressure with the dynamic processes of emulsification, solubilization and molecular diffusion. The results demonstrate the crucial role of emulsification/solubilization in the imbibition process.

Cocamidopropyl hydroxyl sulfobetaine (CHSB) is one of the most promising foaming agents for high-salinity reservoirs because the salt in place facilitates its foam stability, even with salinity as high as 2 × 10⁵ mg/L. However, the synergistic effects between CHSB and salt have not been fully understood. This study utilized bulk foam tests and thin-film interferometry to comprehensively investigate the macroscopic and microscopic decay processes of CHSB foams with NaCl concentrations ranging from 2.3 × 10⁴ to 2.1 × 10⁵ mg/L. We focused on the dilatational viscoelasticity and dynamic thin-film thickness to elucidate the high-salinity-enhanced foam stability. The increase in dilatational viscoelasticity and supramolecular oscillating structural force (Π_OS) with salinity dominated the superior stability of CHSB foam. With increasing salinity, more CHSB molecules accumulated on the surface with a lower diffusion rate, leading to high dilatational moduli and surface elasticity, thus decelerating coarsening and coalescence. Meanwhile, the number density of micelles in the thin film increased with salinity, resulting in increased Π_OS. Consequently, the energy barrier for stepwise thinning intensified, and the thin-film drainage slowed. This work conduces to understand the mechanisms behind the pronounced stability of betaine foam and can promote the widespread application of foam in harsh reservoirs.

Lignosulfonate, a byproduct of the pulp and paper industry, has been used in the oil-well drilling industry for a significant amount of time. Lignosulfonate and its derivatives serve different roles in the oil-well drilling industry because of their unique structures and properties. This review summarizes lignosulfonate and its derivatives, including lignosulfonate complexed with metal ions, lignosulfonate graft copolymers, lignosulfonate-tannin complexes, and other lignosulfonate-containing composites, in terms of their preparation, properties, and potential applications in oil-well drilling industry. It provides readers with a quick review of existing studies in this area and some inspirations for future studies pertaining to the utilization of lignosulfonate-based materials in the oil-well drilling industry.