The effects of CO₂-water-rock interactions on the injectivity and safety of CO₂ geological storage have drawn wide attention. The geochemical reaction mechanisms in carbonate formations after CO₂ injection are still controversial. To better understand the transformation of injected CO₂ in carbonates and the involved geochemical reactions, we first conducted autoclave experiments reproducing the in-situ conditions of the Lianglitage Formation, Yingshan Formation, and Qiulitage Formation at the Tazhong Uplift in the Tarim Basin. We then established a batch model using TOUGHREACT-ECO2H, validated with the experimental results, to simulate the long-term CO₂-water-rock interactions. It was found that the initial mineral compositions and water chemistry have important effects on the CO₂-water-rock interactions in carbonate formations. The experiment results show that the dissolution of calcite and dolomite dominates in the early reaction period. However, we still observed some secondary minerals, such as ankerite, montmorillonite, calcite, and dolomite. The CO₂-water-rock reactions can be more dramatic when the contents of calcite and dolomite in carbonates are closer. Moreover, the long-term simulation results show that calcite, magnesite, and hematite are the main formed secondary minerals, whereas dolomite is the major dissolved mineral. This study is helpful for a better understanding of the CO₂ mineral trapping mechanism in carbonate formations.

Under the dual carbon mission, more and more coal mines will face shutting down in the future and stop treating mine water drainage, which, if it escapes, may cause severe secondary damage to the local groundwater quality. Wudong Coal Mine is a currently active subsurface coal mine in Xinjiang, China, that shows high-salinity characteristics. To forecast and discuss future possible groundwater quality damages and potential solutions, we here introduce a model prediction study on the effects of water pollution by coal mine drainage. The study protocol first involves creating a calibrated 2D groundwater flow model by use of FEFLOW software, then designing several flow and solute transport prediction analyses under changing mine water drainage conditions, different pollution source areas and water treatment pumping wells to discuss future prominent flow and transport behavior, as well as water treatment-affecting factors. It has been shown that mine water drainage plays a critical role in maintaining the mine water solute distribution, as without mine draining, local flow and solute distribution change dramatically, altering the groundwater capture zone, and may change the plume-migrating direction from upstream to downstream. A larger pollution source could produce a higher concentration of pollutants and a larger pollution-coverage area. To reduce pollutant concentrations, mine water treatment pumping wells with higher pumping rates can be applied as a useful remedial measure to effectively prevent the pollutant plume front from reaching the important drinking and irrigation water source of the region, Urumqi River. The results of this study can give important suggestions and decision-making support for authorities focused on water treatment and environmental protection decision-making in the region.

In carbon sequestration in saline aquifers, many factors affect multiphase fluid migration and reservoir pressure change. This study developed a high-resolution three-dimensional numerical model to investigate large-scale CO₂ geological storage in the Ordovician carbonate aquifer of the Tarim Basin, China. This study focuses on the quantitative prediction of CO₂ plume migration, multiphase flow interactions between supercritical CO₂ and brine, and formation pressure evolution under coupled injection operations. Injection strategies were compared by constant rate (CR) and variable rate (VR) injection, and two caprock morphology-type selection by placing wells into monocline traps (wells 1/3/5) and anticline traps (wells 2/4) with varying limb dip angles and closure depths. The results demonstrate that both injection speed and caprock morphology strongly control CO₂ trapping evolution and storage security. At the end of the 500-year simulation, the dissolved-CO₂ migration distance followed the order CR > VR, indicating that, under the studied conditions, VR injection most effectively limited the lateral spread of dissolved CO₂ and thereby enhanced dissolved-CO₂ immobilization. In addition, CR and VR injection schedules have a subtle impact on long-term pressure change; Across all cases, formation pressure remained below the caprock breakthrough pressure. CR injection promotes the fastest CO₂ dissolution and pressure dissipation but yields the weakest long-term immobilization, whereas VR injection trades early dissolution rate for more effective plume containment. This result indicates that injection-strategy selection should be matched to dominant site controlled near-term pressure management versus long-term containment and to the trapping behavior imposed by caprock morphology. This study provides a mechanistically grounded optimization framework linking injection-speed control and caprock morphology to the coupled evolution of pressure-buildup safety and long-term CO₂ immobilization, supporting CCUS decision-making in the Tarim Basin.

Levee-like features are critical for water regulation in arid regions, but their automated extraction from remote sensing imagery remains challenging due to the scarcity of high-resolution labeled datasets. This data descriptor introduces a high-resolution remote sensing image dataset for semantic segmentation of levee-like features. The dataset covers 11 regions across Xinjiang and Gansu Province in northwestern China. It includes 459 single-phase base images with a spatial resolution of 0.50 m, as well as multi-temporal images of the same regions captured at different times. All annotations were manually drawn in polygon mode using the LabelMe tool and converted into YOLO format label files. The dataset adopts a strict strategy to prevent data leakage: first, training, validation and test sets are divided based on single-phase images, and then multi-temporal images are allocated to the corresponding data subsets according to their spatial locations. The dataset has been publicly released on the ScienceDB platform under the CC BY 4.0 license. YOLO and U-Net segmentation experiments on the test set achieved promising results, demonstrating its usability for levee-like feature segmentation. This dataset can provide fundamental data support for research on levee-like feature extraction, remote sensing change detection, and cross-region model transfer learning.

CO2 storage in saline aquifers is one of the most effective ways of geological carbon sequestration. In order to better understand brine-CO2-rock interaction in carbonate reservoirs, 4 series of autoclave experiments with the carbonate rock powder samples with injection of super critical CO2 have been performed. Two core samples were collected from the TC1well at the depth of 4030m (Lianglitage Formation) and 5100m (Qiulitage Formation), and another two samples from corresponding formation  and with varing mineral content were collected from the Yijianfang outcrop and Xiaoerbulake outcrop in Tazhong-Uplift, Tarim Basin, China. The experimental conditions simulate the environment of the reservoir around 4000m depth at the Tazhong Uplift with 25Mpa and 120 degree, where the brine water is CaCl2 type with TDS equal to 135g/l. The FESEM,EDS, XRD, ICP-OES analysis have been performed to examine the mineral chemical composition, morphology and water solution change. The results show that, in all cases after the injection of CO2, with CO2 dissolution, pH shows a decrease at the beginning days of the experiments and start to rise, becomes stable at the end of the experiment. Where as, with the dissolution of the minerals results in continuous increase in electrical conductivity. The SEM analysis demonstrates the dissolution of the calcite and dolomite resulted in a rough surface structure and the sharp edges of calcite and dolomite are dissolved. Also, it is able to observe the formations of new micropores and formation of secondary minerals such as ankerite. In the fluid analysis, Ca2+ is the dominant dissolved cation and originated from calcite and dolomite dissolution. The concentration of Ca2+will first increase sharply and then decreases, whereas concentration of Mg2+ will increase slowly, which means calcite dissolution take places faster than dolomite dissolution. Numerical modeling has been applied to validate the experimental observations with corrected reaction rate. These results can be used for numerical calculation of mineral trapping over long period. This study is helpful for implementation of carbon sequestration plan in Tarim Basin, China.

Groundwater protection is critical for sustainable water resource management, particularly in arid regions. However, current zoning methods show challenges such as data bias of expert-driven models and limited interpretability of machine learning models. To address these issues, using 16 hydrological datasets from Yulin City in northwest China, two methodological frameworks were constructed: one combining the traditional Analytic Hierarchy Process (AHP) with Geographic Information System (GIS), and the other combining machine learning methods with Principal Component Analysis (PCA) and Self-Organizing Map (SOM). Rather than proposing a novel hybrid model, this study establishes a comparative framework that serves as a prescriptive decision protocol: AHP-GIS provides a transparent, defensible basis for regulatory implementation, while PCA-SOM with SHAP analysis offers interpretable insights into data-driven patterns. The zoning results of these methods show high spatial consistency (81.10%) with some differences (18.90%). Both methods effectively captured medium to key protection zones, particularly in areas characterized by high groundwater yield, good water quality, and ecological sensitivity. SHAP analysis further explained methodological divergences: pollution resistance and mining intensity were the primary drivers of key protection zone in PCA-SOM (12.89%), contrasting with the expert-assigned priority to functional zone and water quality in AHP-GIS (20.06%). This dual-framework approach overcomes the limitations of individual methods by using AHP-GIS to address the black-box nature of machine learning for policy applications, while using PCA-SOM to counteract the subjective bias inherent in expert weighting. Comparisons reveal fundamental trade-offs between transparency and objectivity, pattern sensitivity, regulatory consistency and adaptability to complex spatial relationships. By providing a decision protocol for method selection based on specific management contexts, our findings offer actionable guidance for overcoming the limitations of current approaches.