This paper evaluates the carbon dioxide sequestration potential in the saline aquifers of the South Qiongdongnan–Yinggehai Basin. By using a hierarchical evaluation method, the assessment is divided into five stages: the basin level, the zone level, the target level, the site level, and the injection level. The study primarily focuses on evaluating the sequestration potential of and identifying favorable zones of saline aquifers at the basin and zone levels. The optimized volumetric method is adopted, based on the integration of multi-source data such as regional geological maps, seismic data, core porosity, and permeability. The results show that the estimated potential of the Yinggehai Basin is 60.6 billion tons at the basin level and 54.6 billion tons at the zone level. Additionally, the estimated potential of the South Qiongdongnan Basin is 261.5 billion tons at the basin level and 234.8 billion tons at the zone level. The suitability evaluation indicates that the Yinggehai Basin is moderately suitable overall, the northern depression of the South Qiongdongnan Basin is suitable, the central uplift is moderately suitable, and the central depression is not suitable. This study provides a scientific foundation for carbon dioxide sequestration in marine basins and introduces novel ideas and methods for future similar research. This is highly significant for subsequent engineering applications and decision-making processes.

The shale-oil reservoir emerges as a significant unconventional energy source, commonly predicted by anisotropic seismic inversion. Considering the intricate nature of shale-oil reservoirs, it becomes imperative to consider uncertainties during anisotropic inversion. An effective approach to address this involves stochastic inversion, specifically the anisotropic Bayesian linearized inversion (ABLI), which characterizes statistical and spatial correlations of subsurface parameters through a crucial multivariate correlation matrix constructed through geostatistics. However, an inevitable challenge in stochastic inversion arises from interference during the calibration of statistical and spatial correlations of subsurface parameters. This challenge becomes particularly pronounced in anisotropic inversion, heightened by the multitude of involved model parameters. Existing decorrelation approaches primarily address statistical correlation, neglecting the impact of spatial correlation. To tackle this issue, a novel multi-parameter decoupling strategy is proposed, formulating decoupling-ABLI (D-ABLI). D-ABLI introduces an advanced decorrelation approach, and uses principal component analysis (PCA) to simultaneously eliminate impact of statistical and spatial correlations on ABLI. The decoupling enhances the inversion accuracy of model parameters in ABLI, particularly for density and anisotropic parameters. The theoretical underpinnings of the decoupling strategy are demonstrated to be reasonable, and the effectiveness of D-ABLI is proved through a theoretical data test and a field data test regarding shale-oil reservoirs. The D-ABLI results offer the capability to estimate fracture density accurately and unveil the distribution of shale oil.

Joint estimation of elastic and petrophysical properties from seismic data and quantification of their uncertainties are critical aspects of reservoir characterization studies. The ensemble smoother with multiple data assimilation (ES-MDA) is proving to be a valuable tool for generating a reliable set of reservoir properties by matching simulated seismic responses with available observations. However, in standard implementations of ES-MDA, when the ensemble size is small, spurious correlations can arise in the cross-covariances of model parameters and seismic data, leading to erroneous parameter updates in inappropriate regions. To mitigate this problem, this paper introduces ES-MDA in conjunction with covariance localization (CL), termed as ES-MDA_CL, which aims to reduce the impact of spurious covariance resulting from small ensembles and provide inversion results with robust error estimation. In the ES-MDA_CL framework, the model parameters are initially generated perturbatively by geostatistical simulations and subsequently updated while constrained by seismic data. The introduction of CL reduces the size of the initial ensembles, thereby increasing the computational efficiency of the entire inversion process. Through synthetic and field data tests, ES-MDA_CL demonstrates satisfactory inversion results with more reasonable computational times compared to ES-MDA. The proposed methodology enables the generation of inversion results with robust uncertainty estimation and holds promise for application to a wide range of geophysical challenges.