CO2 storage well rate optimisation in the Forties sandstone of the Forties and Nelson reservoirs using evolutionary algorithms and upscaled geological models
CO2 storage well rate optimisation in the Forties sandstone of the Forties and Nelson reservoirs using evolutionary algorithms and upscaled geological models
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使用进化算法和升级地质模型对 Forties 和 Nelson 储层的 Forties 砂岩进行 CO2 封存井速率优化
DOI:
10.1016/j.ijggc.2016.04.011
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发表时间:
2016
影响因子:
3.9
通讯作者:
Babaei M
中科院分区:
文献类型:
--
作者:
Babaei M
Optimisation is particularly important in the case of CO2storage in saline aquifers, where there are various operational objectives to be achieved. The storage operation design process must also take various uncertainties into account, which result in adding computational overheads to the optimisation calculations. To circumvent this problem upscaled models with which computations are orders of magnitude less time-consuming can be used. Nevertheless, a grid resolution, which does not compromise the accuracy, reliability and robustness of the optimisation in an upscaled model must be carefully determined. In this study, a 3D geological model based on the Forties and Nelson hydrocarbon fields and the adjacent saline aquifer, is built to examine the use of coarse grid resolutions to design an optimal CO2storage solution. The optimisation problem is to find optimal allocation of total CO2injection rate between existing wells. A simulation template of an area encompassing proximal-type reservoirs of the Forties-Montrose High is considered. The detailed geological model construction leads to computationally intensive simulations for CO2storage design, so that upscaling is rendered unavoidable. Therefore, an optimal grid resolution that successfully trades accuracy against computational run-time is sought after through a thorough analysis of the optimisation results for different resolution grids. The analysis is based on a back-substitution of the optimisation solutions obtained from coarse-scale models into the fine-scale model, and comparison between these back-substitution models and direct use of fine-scale model to conduct optimisation.
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