Geological carbon storage is a promising solution to reduce the CO₂ concentration in the atmosphere to ameliorate the effects of global warming from the greenhouse effect. Among feasible storage options, deep saline aquifers are believed to have the largest storage capacity for the gas collected from industrial processes. The first CO₂ storage project at a commercial scale in a saline aquifer is in the Sleipner field of the Utsira storage formation in Norway. The long ongoing storage operation in the Sleipner field has been the subject of several past studies attempting to recreate the observed injected CO₂ plume migration behaviour. History matching is a method to adjust the input parameters of the model in a way to minimise the mismatch between the simulated and the actual production data in reservoir engineering and applicable to carbon sequestration. Typical parameters adjusted in history matching are porosity, absolute and relative permeability data. In this study, we used an adjoint‐based optimisation tool and showed the importance of caprock morphology in finding an accurate plume match. Using a set of synthetic models, we initially minimised the mismatch between the observed and simulated CO₂ plume outline by modifying the caprock topographical details. After testing the optimisation tool on the synthetic models, we applied the methodology to the Sleipner benchmark 2019 model and improved the plume match by locally adjusting caprock elevation within seismic detection limits. We subsequently improved the match by calibrating porosity, permeability, CO₂ density and injection rate together in an experiment in which we calibrated all the parameters, including the caprock morphology, to find a better match. The results showed an improvement of around 8% (compared with the original model) in the plume match resulting from an average absolute elevation change of 3.23 m in the model while keeping the other parameters constant. Calibrating the porosity, permeability, CO₂ density and injection rate resulted in a 5% improvement in the match, and once caprock morphology was included in the optimisation process, the match improvement increased by 16%. We changed the caprock elevation within a range lower than the seismic detection limit, and results showed that even a few metres variations in the elevation have significant impacts on the plume migration and trapping mechanism in the Sleipner model. The method presented in this work results in a better match than the original seismic data for the Sleipner model. © 2020 The Authors. Greenhouse Gases: Science and Technology published by Society of Chemical Industry and John Wiley & Sons, Ltd.

中文翻译：

---

使用优化方法分析盖层形态在Sleipner CO2羽流历史拟合中的作用

地质碳储存是一种有希望的解决方案，它可以降低大气中的CO ₂浓度，从而减轻温室效应带来的全球变暖的影响。在可行的存储方案中，据信深盐含水层对于从工业过程中收集的气体具有最大的存储容量。在盐水层中商业规模的第一个CO ₂储存项目是在挪威Utsira储存地层的Sleipner油田。在Sleipner油田中长期持续的存储操作一直是过去几项研究的主题，这些研究试图重现观察到的注入CO ₂羽流迁移行为。历史匹配是一种以最小化油藏工程中模拟和实际生产数据之间的不匹配且可应用于碳固存的方式来调整模型输入参数的方法。在历史匹配中调整的典型参数是孔隙度，绝对和相对渗透率数据。在本研究中，我们使用了基于伴随的优化工具，并显示了盖层形态对寻找准确羽流匹配的重要性。使用一组综合模型，我们最初将观察到的和模拟的CO ₂之间的失配最小化通过修改盖岩的地形细节来绘制羽状轮廓。在综合模型上测试了优化工具之后，我们将该方法应用于Sleipner基准测试2019模型，并通过在地震探测范围内局部调整盖层高度来改善羽流匹配。随后，我们在一个实验中对孔隙度，渗透率，CO ₂密度和注入速率进行了校准，从而改善了匹配度，在该实验中，我们对包括盖岩形态在内的所有参数进行了校准，以找到更好的匹配度。结果表明，由于模型中平均绝对高程变化为3.23 m，而其他参数保持不变，因此羽流匹配提高了8％（与原始模型相比）。校准孔隙率，渗透率，CO ₂密度和注入速率使匹配度提高了5％，并且在优化过程中包括了盖洛克形态后，匹配度提高了16％。我们在低于地震检测极限的范围内更改了盖层标高，结果表明，即使是几米的标高变化，也会对Sleipner模型中的羽流迁移和捕集机制产生重大影响。这项工作中提出的方法比Sleipner模型的原始地震数据具有更好的匹配性。©2020作者。温室气体：化学工业协会和John Wiley＆Sons，Ltd.出版的科学和技术。