Large-scale deployment of Carbon Capture and Storage (CCS) will require a commensurately large number of sites. Efficient screening methods are needed to create investment assurance and focus efforts on the most promising sites. The problem is similar to petroleum exploration, for which there are well-developed (though seldom published) workflows, including Common Risk Segment (CRS) mapping. In brief, the process requires 1) defining the key play elements; 2) identifying candidate geologic intervals for each; 3) creating fact-based maps for those intervals; 4) determining minimum criteria for the success of each element; 5) reinterpreting the fact-based maps in terms of chance of success; and 6) combining the individual maps to form a composite, basin-scale view of prospectivity. In this paper, we adapt the CRS process to screening for CO₂ storage sites. Critically, we redefine the process in terms of cost of characterization and development, rather than chance of success. For illustration, we apply the process to the example of the Lower Miocene on the Texas and Louisiana Gulf Coast. We show that the predictions are consistent with historic hydrocarbon production volumes and rates. The power of the CRS method is that it creates a systematic approach to geologic evaluation and translates complex, multidimensional analysis into clear, graphical and easily comprehended business inputs. The result highlights sweet spots and identifies critical risks, suggesting a focus for further data collection and analysis. The method developed here can be applied to both surface and subsurface factors anywhere that there is interest in geologic storage of CO₂.

碳捕集与封存 (CCS) 的大规模部署将需要相当多的站点。需要有效的筛选方法来建立投资保证并将努力集中在最有前途的地点。这个问题类似于石油勘探，有完善的（虽然很少发布）工作流，包括共同风险段 (CRS) 映射。简而言之，该过程需要 1) 定义关键播放元素；2) 为每个确定候选地质层段；3) 为这些区间创建基于事实的地图；4) 确定每个要素成功的最低标准；5）根据成功机会重新解释基于事实的地图；6) 将单独的地图组合起来，形成一个复合的、盆地尺度的前景视图。在本文中，我们采用 CRS 流程来筛选 CO_2个存储站点。至关重要的是，我们根据表征和开发的成本而不是成功的机会重新定义了流程。为了说明，我们将该过程应用于德克萨斯州和路易斯安那州墨西哥湾沿岸的下中新世的示例。我们表明预测与历史碳氢化合物产量和速率一致。CRS 方法的强大之处在于它创建了一种系统的地质评估方法，并将复杂的多维分析转化为清晰、图形化且易于理解的业务输入。结果突出显示了最佳点并识别了关键风险，建议重点关注进一步的数据收集和分析。此处开发的方法可应用于对 CO ₂地质储存感兴趣的任何地方的地表和地下因素.