油藏数值模拟自动历史拟合方法——以Nelson油田为例

针对北海中部Nelson油田,研究油藏数值模拟自动历史拟合方法。完整的油藏自动历史拟合工作流程包括选择要调整的油藏变量、选择参数调整方法、自动历史拟合、数据分析、组合最优结果得到最优的油藏模型集5个步骤。以净毛比、水平渗透率、垂直渗透率为调整参数,采用全局单变量法、区域多变量法、局部多变量法等3种油藏参数调整方法,并结合生产数据和时移地震数据,对Nelson油田进行自动历史拟合。结果表明,局部参数调整方法减少了模拟中模型的数目和计算时间,提高了模拟精度,使得油藏中油水产量拟合误差大大降低,在Nelson油田应用效果最好。全局单变量法适合于历史拟合参数相互独立的情况;对于井网密度大且井间关联性强、历史拟合参数相互影响的油藏,最好选用区域多变量法进行参数调整;对于油藏历史拟合参数相互影响但每个选定调整区域相互独立的油藏,局部多变量法更加适用。

Schemes for automatic history matching of reservoir modeling: A case of Nelson oilfield in the UK

Schemes for automatic history matching of reservoir modeling are studied for the Nelson oilfield in the Central North Sea. A complete workflow of automatic history matching involves selection of reservoir variables that require modification and parameter updating schemes, automatic history matching, data analysis, and combination of the best results to obtain an ensemble of best reservoir models. Automatic history matching of Nelson field is conducted using production and time-lapse seismic data, with global single-variable approach, regional multi-variable approach and local multi-variable approach as updating schemes, net to gross, horizontal and vertical permeability as updating parameters. Three parameter updating schemes, i.e. global single variable approach, regional multi-variable approach and local multi-variable approach, are introduced, in order to reduce the number of simulations significantly and make automatic history matching workable. They are applied for history matching of Nelson field using production and time-lapse seismic data. It is revealed that local multi-variable approach can effectively improve history matched results byis very useful for reducing the number of simulation models, saving computing time and increasing the simulation precision. Global single-variable approach is only a suitable parameter updating scheme for cases where the history matching parameters are independent. Regional multi-variable approach is suitable for the cases where there is strong dependency between properties chosen for updating, and there are wells very close together with strong interaction. Local multi-variable approach is very useful when the history matching parameters are dependent but each selected region for updating is independent of others.