A Statistical Learning Approach to Model the Uncertainties in Reservoir Quality for the Assessment of CO2 Storage Performance in the Lower Permian Rotliegend Group in the Mid North Sea High Area
A Statistical Learning Approach to Model the Uncertainties in Reservoir Quality for the Assessment of CO2 Storage Performance in the Lower Permian Rotliegend Group in the Mid North Sea High Area
复制标题
一种统计学习方法，用于模拟储层质量的不确定性，以评估北海中高地区下二叠统 Rotliegend 群的 CO2 封存性能
DOI：
10.1016/j.egypro.2017.03.1592
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Govindan R
中科院分区：
文献类型：
--
作者：
Govindan R
已经确定，英国第27-29象限北海中部高地地区的Rotliegend砂岩储层具有国家重要性的大规模CO2储存潜力。在本文中，作者开发了一个储层模型，利用广泛的数据集，从地震解释和岩心分析。应用先进的统计学习方法消除储层质量空间分布的不确定性。该模型被用来评估CO2注入性能和thusfar得到的初步结果表明，在可用的存储容量的承诺。
It has been identified that the Rotliegend sandstone reservoir in the Mid North Sea High region, in the UK Quadrants 27-29, has a large-scale CO2storage potential of national importance. In this paper, the authors develop a reservoir model using extensive datasets available from seismic interpretations and core analysis. An advanced statistical learning approach was applied to characterise the uncertainties in the spatial distribution of reservoir quality. The model was used to assess the CO2injection performance and the preliminary results obtained thusfar indicate promise in the available storage capacities.