Abstract

Enhancing shale oil recovery during CO₂ injection is crucial for advancing carbon neutrality. However, the strong affinity between the surface of organic pores and hydrocarbons limits the effective oil displacement efficiency. Therefore, it is imperative to find effective methods to improve shale oil recovery. In response to this challenge, this study employs electric field technology to investigate its impact on shale oil recovery during CO₂ injection. In this study, molecular dynamics (MD) simulations were used to study the effect of an electric field on CO₂-enhanced oil recovery in shale nanopores. A composite system consisting of organic nanopores, multicomponent oil, and CO₂ was adopted. Simulations were conducted after applying different electric field intensities (0 V, 5 V, 10 V, and 20 V) to the crude oil. The results show that the electric field increases the difference in interaction energy between CO₂ – oil and oil – pore walls, enhancing the ability of CO₂ molecules to displace hydrocarbons from the pore surface. The displacement efficiency improved after different electric field intensities were applied. Specifically, the electric field intensities of 5 V, 10 V, and 20 V increased the displacement efficiency by 3.20%, 2.85%, and 1.61% respectively compared to the case of 0 V. These findings indicate that the electric field is an effective strategy to overcome oil retention at the pore scale, and its directional application is a key parameter for optimizing CO₂ – enhanced oil recovery in shale formations. This method not only improves the extraction efficiency of hydrocarbons but also supports the achievement of carbon neutrality goals by enhancing the CO₂ sequestration potential.

Abstract

CO2 injection into gas reservoirs offers dual benefits of enhancing gas recovery and achieving carbon storage. However, the effectiveness of this process varies significantly across different geological settings, making it essential to identify the primary geological factors. Using the DF gas field with a weak edge-bottom aquifer in the South China Sea as a case study, a numerical model was established for CO2-enhanced gas recovery (CO2-EGR), which incorporated key mechanisms such as phase behavior, CO2 diffusion, and dissolution. Various geological factors were systematically evaluated to determine their influence on EGR. Results indicate that reservoir heterogeneity, thickness and edge-bottom aquifer are the dominant controlling factors, whereas permeability, rhythmic type, and dip angle (0~5°) exhibit comparatively minor effects. Horizontal heterogeneity, in particular, exerts the strongest impact on recovery performance. Reservoirs with thin layers (<10 m), large aquifers (5~10 times), or low permeability (<10 mD) show higher potential for EGR, primarily due to their lower recovery under depletion development. Composite and reverse rhythmic reservoirs are more favorable for CO2 injection, with improved recovery observed when CO2 is injected into lower structural positions. Well placement and pattern design for CO2 injection should prioritize reservoir heterogeneity, thickness, and aquifer characteristics. In the DF gas field, a configuration with three CO2 injection wells is projected to enhance gas recovery by 12% and sequester over one million tons of CO2 annually, supporting the green and low-carbon development of offshore gas fields.

Abstract

Deep saline aquifers remain a critical option for large-scale CO2 storage to mitigate global climate change. The Permian saline aquifer in the Tiaohu Formation of the Santanghu Basin has emerged as an ideal target for CO2 geological sequestration due to its superior storage conditions. This study employs the ECO2N module within the TOUGH2-PetraSim software to establish a numerical model of multiphase flow in a basalt-tuff reservoir system. The research reveals the fluid migration characteristics following CO2 injection into the saline aquifer, with a focus on analyzing the influence of well orientation on CO2 plume migration, including the variation patterns of CO2 gas saturation and solubility. Research demonstrates that horizontal wells enable more uniform spatial distribution of CO2.

Abstract

Optimization of integrated carbon capture processes is considered essential for addressing the challenges of low capture efficiency and high costs associated with low-concentration CO2 streams, thereby advancing low-carbon industrial practices. In this context, an adsorption-coupled cryogenic liquefaction process is proposed. Process efficiency and system energy consumption under diverse feed conditions were systematically evaluated via simulation. Techno-economic assessment was performed considering the trade-off between capture performance and energy requirements. Simulation results indicate that at feed CO2 concentrations of 5–15%, capture rates of 80.60–81.50% were achieved with product purity of 98.60–98.94%, while energy consumption ranged from 4.012 to 4.494 GJ/tCO2. When optimizing for a 90% capture rate, product purity increased by 0.1% on average, though a 7.34% average increase in energy consumption was observed. Integration of this process with recoverable cold energy from gas-fired power plants could reduce energy consumption by >2 GJ/tCO2, significantly lowering operational demands. This approach provides a validated pathway toward decarbonizing industrial systems, demonstrating both theoretical and practical significance for achieving carbon neutrality.

Abstract

The development of ultra-deep heavy oil reservoirs currently faces challenges including steam injection difficulties, poor fluid mobility, and significant thermal losses in oil layers. The target reservoir had undergone various enhanced oil recovery (EOR) methods such as steam thermal recovery and viscosity reducer huff-and-puff stimulation. However, none of these approaches had achieved breakthrough in productivity. This highlights the critical importance of exploring hot CO2 fluid injection technology specifically tailored for ultra-deep heavy oil reservoir development. This study systematically conducted the following experimental investigations: (1) Three-stage (10 %, 30 %, 60 %) CO2 gas injection expansion experiments with heavy oil, coupled with rheological characterization under varied temperature-pressure conditions. (2) Precise measurements of CO2 diffusion coefficients and solubility parameters in heavy oil systems, including thermal conductivity determination across temperature/pressure gradients. (3) Experimental analysis of pressure-time dependent CO2 extraction effects on heavy oil component fractionation. (4) Microscopic visualization experiments elucidating the CO2 displacement mechanisms in ultra-deep heavy oil reservoirs at pore-scale resolution. Experimental observations revealed that increasing the CO2 injection ratio leads to a systematic decrease in crude oil density (from 1.0646 to 0.9863 g/cm³ at 100 °C) accompanied by a 4.65-12.47 % expansion in volume coefficient. The native heavy oil exhibited shear-thinning behavior consistent with the Herschel-Bulkley model, whereas CO2-saturated samples transitioned to Newtonian flow characteristics with viscosity reductions exceeding 75 % at 10 s⁻¹ shear rate. Notably, diffusion coefficients in ultra-deep heavy oil reservoirs (depth > 2000 m, viscosity > 1×10⁴ mPa·s) were measured to be approximately one order of magnitude lower than those in conventional crude oil. Thermal conductivity analysis of the CO2-heavy oil mixture showed progressive reductions with elevated temperature and pressure. Compositional monitoring identified selective extraction of C₅ – C₇ hydrocarbons during initial CO2 interaction, while higher carbon-number components (C₁₂ – C₂₀) required increased pressure for effective mobilization. The pressure-driven enhancement of CO2 extraction efficiency in ultra-deep heavy oil necessitated extended contact durations (> 24 h) to reach equilibrium. In the process of heavy oil displacement, CO2 was prone to gas channeling, while CO2 synergist had the effect of emulsifying and dissolving viscosity reduction and forming foam oil to expand sweep efficiency in the process of displacement.

Abstract

On March 20, 2025, China’s Ministry of Ecology and Environment issued “the Work Plan for Including the Steel, Cement, and Aluminum Smelting Industries in the National Carbon Emission Trading Market”, explicitly incorporating these three sectors into the national carbon market management framework, covering an additional 3 billion tons of CO2 emissions. This move holds significant implications for the development of the carbon market and the application of CCUS technologies in these industries. Current research on CCUS investment incentives primarily focuses on sectors like power and oil & gas, with limited studies addressing policy mechanisms to promote CCUS adoption in steel enterprises. Moreover, due to the high costs of CCUS investments, existing research exhibits a pronounced “technology-oriented” bias, overemphasizing techno-economic analyses while neglecting financing-related policies and financial innovations, particularly the financing decisions of banks and other financial institutions.
To facilitate financing for steel plants’ CCUS retrofits and ensure the implementation of emission reduction technologies, this study proposes a “government subsidy + bank green credit” financing incentive policy. A tripartite evolutionary game model is constructed involving steel enterprises, governments, and financial institutions. Banks decide whether to issue green credit based on risk-return assessments, governments determine their regulatory stance, and enterprises weigh retrofit costs against benefits to decide on CCUS adoption. Carbon allowance trading is introduced to examine the system‘s evolutionary dynamics and stable strategies. Key findings include:
(1) Initial strategy proportions have minimal impact on simulation outcomes, with the stable equilibrium being (retrofit, grant green credit, proactive governance).
(2) Government subsidies and penalties are pivotal in incentivizing banks to extend green credit and enterprises to adopt retrofits, though excessive fiscal pressure may discourage proactive governance.
(3) Higher carbon social costs simultaneously strengthen government proactivity and bank lending willingness.
(4) Green credit returns drive bank lending, while rising lending costs, though dampening bank willingness, can pressure governments to act proactively and enhance enterprise retrofit motivation.
(5) Under carbon trading, enterprise willingness to retrofit increases with carbon prices.
Integrating game theory with carbon market mechanisms, this study proposes a dynamic CCUS financing decision framework for the steel industry, offering theoretical insights for quantifying policy synergies and designing green finance policies for high-emission sectors under the “dual carbon” goals.

Abstract

Compositional simulation plays a vital role in understanding the behavior of multiple components within the captured CO2, such as impurities or various injection scenarios, helping optimize the storage process and ensure its long-term safety and effectiveness. A new ecpa module in MATLAB Reservoir Simulation Toolbox (MRST) has been developed based on the electrolyte cubic-plus-association equation of state (e-CPA) to simulate enhanced gas recovery and CO2 storage with CO2 injection. The ecpa module combines Fickian diffusion, Langmuir adsorption, and the e-CPA equation of state to accurately describe phase equilibria and transport phenomena in complex systems such as CO2-CH4-H2O-NaCl system. The e-CPA equation of state demonstrates superior accuracy over conventional models (e.g., PR EoS) in predicting vapor-liquid equilibria and phase behavior across a wide range of pressures and temperatures. For the CO2-H2O system, the e-CPA model achieves lower average absolute relative deviations (AARD: 5.93% for CO2 solubility, 9.54% for H2O content in gas) compared to Tsivintzelis’s model (AARD: 8.52% and 15.55%, respectively). In the Barnett shale case study, accounting for sorption and diffusion increases CHâ‚„ mass flow rates by 15-20%, highlighting their critical role in unconventional reservoir simulations. Comparisons with ECLIPSE and CMG (GEM) reveal strong agreement in CO2-CH4 mixing behavior, recovery factors, and breakthrough times. MRST (e-CPA) matches CMG’s predictions for CH4 production and CO2 storage, validating its matrix-fracture coupling and thermodynamic consistency. The ecpa module, consolidated into the MRST framework, provides an open-source, state-of-the-art tool for simulating multicomponent flow in geological carbon storage, shale gas recovery, and enhanced hydrocarbon extraction. Its release in MRST 2025a will enable broader academic and industrial adoption, fostering advancements in subsurface energy systems.

Abstract

The reinjection technology of impurity-containing CO₂ represents a critical challenge to the safety and economic viability of carbon capture and storage (CCS) systems. This study systematically analyzes the influence patterns of impurities on CO₂ phase behavior, flow characteristics, and reinjection energy consumption through the establishment of thermodynamic phase equilibrium models, flow assurance models, and energy consumption optimization models. By utilizing the Peng-Robinson (PR) equation of state and Aspen HYSYS software, the differential regulatory mechanisms of various impurities (e.g., N₂, H₂) on CO₂ critical point shifts, phase envelope expansion, and property parameters (density, viscosity, thermal conductivity) was revealed. Through OLGA multiphase flow simulations, we quantified the low-temperature risks induced by the Joule-Thomson effect and hydrate formation potential, demonstrating that impurity-containing CO₂ exhibits 2.66 °C less throttling temperature drop compared to pure CO₂, with the critical hydrate formation temperature decreasing from 10 °C to 8 °C. For the Snohvit field case in Norway, a reinjection scheme integrating four-stage compression with interstage air cooling optimization was proposed. This high-temperature reinjection strategy (50 °C) achieved 24.8% lower total energy consumption than conventional low-temperature injection (20 °C), while recommending further efficiency improvements through waste heat recovery and intelligent control. The findings provide theoretical foundations and technical pathways for the safe transportation and efficient reinjection of impurity-containing CO₂.

Abstract

It is crucial to expose the miscible mechanism of CO2 and crude oil for the development of ultra-deep reservoirs. The influence of oil composition and electrical properties of rock surface on the miscible process of CO2 and crude oil (C7H16, C7H16S, C7H17N, C7H16O, C7H14O2, C9H20, C11H24) at 453 K and 90 MPa is investigated by using the molecular dynamics simulation. The simulation results indicate that the adsorption of CO2 and C7H16 on the calcite surface limits the diffusion of CO2 and C7H16 along the Z direction. Meanwhile, high pressure could weaken the effect of temperature on the adsorption of CO2 and C7H16 on the calcite surface. In addition, the functional group and long chain of crude oil molecules adversely affect the miscibility between crude oil and CO2. Moreover, the positively charged surface could promote the miscibility of C7H16 and CO2. The miscibility of C7H14O2 and CO2 could be facilitated in nanoslits with the same surface charges while it could be hindered in nanoslits with the opposite charges. This work could shed new light on the further research on the application of CO2 flooding in ultra-deep reservoirs.

Abstract

CO2-responsive nanoparticles are injected into the formation to enhance oil recovery by improving reservoir heterogeneity and increasing macro sweep efficiency. As the particles migrate within the pore space, their size increases with the rise in the pH of the aqueous phase. Consequently, particles cause plugging when the pH decreases. However, the lack of a suitable model for particle migration limits the optimization of particle injection. An easy-to-understand and effective description of the particle migration process is essential for making accurate predictions.
This research introduces a novel model for particle migration. First, a pore network model is used to represent the pore structure. Then, by considering changes in particle size due to pH and treating the total particle concentration as an independent variable, Markov chain theory is applied to develop quantitative relationships between the amount of plugged particles and the total number of particles. Finally, a new control model of particle migration is established, and a comparison between theoretical predictions and experimental results is provided to validate its accuracy.