CO₂ geological storage combined with deep saline water recovery technology (CO₂-EWR) is one of the most effective ways to reduce carbon emissions. Due to the complex structural features, it is difficult to use CO₂-EWR technology in Huaiyin Sag, Subei basin, East China. In this study, the multi-source information superposition evaluation technology of GIS was utilized for the selection of CO₂ storage sites and water displacement potential target areas in this area, which mainly focused on the sandstone reservoirs of Cretaceous Pukou Formation. Based on the results, a three-dimensional injection–extraction model was established. Various scenarios with different production/injection well ratios (PIR) were simulated. Research has shown that the suitability of the surrounding site of Huaiyin Power Plant can be divided into two levels: relatively suitable and generally suitable; the area in the generally suitable level accounts for more than 80%. At a PIR of 1, CO₂ is distributed asymmetrically, whereas at PIRs of 2 or 4, CO₂ is distributed symmetrically. When the number of production wells is constant, a higher injection rate results in a faster expansion rate of the CO₂ plume. This means that the time taken for the CO₂ plume to reach the production wells is shorter. Reservoir pressure increases rapidly after more than 60 years of CO₂ injection at lower PIR values, while at higher PIRs, reservoir pressure eventually stabilizes. Higher PIR values correspond to higher gas saturation, indicating a greater capacity for CO₂ sequestration with more producing wells. When PIR = 4, the total CO₂ injection increased by 55.73% compared to PIR = 1. However, the extraction of saline decreases with an increase in the number of producing wells, resulting in a decrease in replacement efficiency. This study provides a theoretical basis and technical support for the implementation of large-scale CO₂-EWR engineering and technology demonstration in this region.

Abandoned production and monitoring wells in depleted oil and gas fields can readily serve as primary leakage pathways for stored CO₂. The temperature, pressure conditions around the wellbore bottom, and CO₂ concentration influence the phase behavior of CO₂ during leakage. This study establishes a 3D wellbore–reservoir coupled model using CO₂ injection data from 1 December 2009, in the DAS area, eastern Cranfield oilfield, Mississippi, USA, to analyze the dynamic evolution of CO₂ leakage along wellbores. Simulations are conducted using the collaboration of ECO2M and ECO2N v2.0 modules. The study examines leakage regimes under varying distances from the injection well and different reservoir temperatures. The results indicate that CO₂ phase changes occur primarily in wells near the injection point or under high-pressure and high CO₂ saturation conditions, usually with a short leakage period due to ice formation at the wellhead. In areas with low CO₂ saturation, prolonged leakage periods lead to significant pressure drops at the bottom, as well as the temperature as a result of the Joule–Thomson effect. Lower reservoir temperatures facilitate smoother and more gradual leakage. These findings provide a theoretical foundation for ensuring the safe implementation of CCUS projects and offer insights into the mechanical explanation of CO₂ geyser phenomena.

Seeking effective measures for the improvement of high-selenium and high-cadmium soils holds significant theoretical and practical importance for sustainable agricultural development. This paper focuses on conducting a site-specific soil survey in the characteristic agricultural product production area of Hefeng County, Enshi Prefecture, Hubei Province. Through field experiments, we compared 14 soil improvement methods across three techniques: chemical passivation remediation, agronomic regulation, and microbial remediation. The study investigated their impacts on rice Cd content, rice Se content, yield, and quality and conducted a comprehensive evaluation of the remediation effects of the different treatments. The experimental results indicate that (1) increasing the content of soil conditioners can enhance rice yields, with Treatment 14 showing the most significant increase, yielding an additional 257.3 kg per mu, representing a 55.62% increase. Treatment 12 also demonstrated a notable yield increase of 95.1 kg per mu, or a 20.55% increase. Lime, sepiolite, and shell powder can effectively reduce rice’s absorption of Cd. Treatment 9 resulted in the lowest Cd content in the rice, at 0.03 mg/kg, with a Cd reduction rate of 92%. The optimal application rates for this Cd reduction were 200 kg/mu of lime, 125 mL/mu of foliar inhibitor, and 50 kg/mu of carbon-silicon fertilizer. Treatment 12 achieved a rice Cd content of 0.11 mg/kg, with a 70% reduction in Cd, bringing the rice Cd content down to below 0.2 mg/kg, which meets the requirements of the National Food Safety Standard: Maximum Levels of Contaminants in Foods. In the comprehensive scoring of all treatments, considering four evaluation indicators—rice Cd content, rice yield, rice quality, and cost—Treatment 12 (300 kg/mu of soil conditioner + 50 kg/mu of carbon-silicon fertilizer) was found to be the optimal treatment through comparative scoring. It demonstrates good potential for ensuring safe rice production and can serve as a reference standard for repairing Cd-contaminated rice paddies in the local area, with promotional value.

Introduction: The volumetric method is the primary approach for calculating geological CO ₂ storage potential, with its accuracy largely dependent on the pore volume of reservoir rocks and the effective storage coefficient. While the precision of reservoir rock pore volume can be enhanced through more sophisticated geological exploration techniques, the current selection of effective storage coefficients lacks a theoretical foundation. Thus, obtaining a more accurate effective storage coefficient is crucial for improving the evaluation precision of CO ₂ geological storage potential. Methods: To explore the factors influencing the effective carbon sequestration coefficient in saline aquifers and accurately assess their storage potential, nine sets of multiphase flow core displacement experiments were conducted using orthogonal design, with porosity, confining pressure, and pressure difference as variables. Results: The results indicate that among these three factors, porosity has the most significant impact on maximum residual CO ₂ saturation. Discussion: Qualitative analysis of water migration in cores during displacement was performed using nuclear magnetic resonance (NMR) T ₂ curves, revealing a close correlation between water movement and pore structure: water in mesopores and macropores is preferentially displaced, whereas water in nanopores and micropores is more resistant to displacement. Additionally, NMR was employed to analyze the maximum residual CO ₂ saturation of artificial cores under different conditions, leading to the establishment of a multiple linear regression equation for maximum residual CO ₂ saturation. By incorporating the volume coefficient derived from numerical simulations, the geological CO ₂ storage coefficient for actual engineering sites can be estimated.

Induced seismicity is a recognized challenge in hot dry rock (HDR) geothermal development. Based on a systematic review of previous studies on induced seismicity mechanisms, characteristics, risk assessment, and mitigation measures, we compiled publicly available data from 13 HDR projects worldwide. Our statistical analysis shows that felt earthquakes occurred in 62% of the projects, and a post-injection “tailing effect” was observed in 54% of the projects. The spatial influence range of induced seismicity is typically within 2 km of the injection well, and the duration of the “Kaiser effect” varies from months to years depending on local conditions. A cross-site comparison of injection parameters suggests that the maximum wellhead pressure may be a more useful indicator than injected volume for estimating the largest possible earthquake magnitude, especially when comparing different tectonic settings. Furthermore, we examine the applicability and limitations of b-value trends, seismogenic indices, and existing maximum magnitude prediction models in seismic risk assessment. Dynamic adjustment of injection parameters based on real-time risk indicators, combined with safer injection schemes, may represent an important research direction for improving the conventional traffic light system. These findings provide a data-driven basis for site-specific safety management of HDR development.

To address the limitations of traditional landslide monitoring methods and the insufficient capability of existing prediction models in capturing nonlinear temporal features, this study proposes a landslide prediction method that integrates InSAR technology with a CEEMD-TCN-BiGRU-Attention model. Taking the Zhamunongba landslide in southeastern Tibet as the research object, a large-scale deformation field was first obtained using SBAS-InSAR technology, from which time-series deformation data of monitoring points were extracted. The original data were then decomposed into trend and periodic deformation components using Complementary Ensemble Empirical Mode Decomposition (CEEMD). Subsequently, the Temporal Convolutional Network (TCN) with causal convolution and residual connections was employed to enhance feature extraction, followed by a Bidirectional Gated Recurrent Unit (BiGRU) to capture complex spatiotemporal dependencies and an attention mechanism to dynamically weight key features, enabling precise prediction of deformation components with distinct characteristics. Notably, for the periodic deformation component, a multi-factor driven prediction mechanism was constructed by integrating cumulative rainfall and average soil moisture at multiple time scales, achieving high-precision periodic deformation forecasting. Ablation experiments demonstrate that the CEEMD-TCN-BiGRU-Attention model excels at capturing temporal correlations and nonlinear features of landslide deformation data, achieving an MAE of 3.58 mm, RMSE of 4.16 mm, and R² of 96.26%. This model outperforms the TCN-BiGRU-Attention, TCN-Attention and single TCN models in prediction accuracy. The proposed landslide prediction method realizes high‑precision forecasting and holds important value for disaster early warning.