论文全文中描述的研究全面比较了七种用于预测伊拉克南部北鲁迈拉油田两口井缺失的岩心测量水平和垂直渗透率和孔隙度数据的插补技术。结果表明，对于所研究的碎屑岩和碳酸盐岩数据集，由链式方程多元插补 (MICE) 和分类回归树 (CART) 组成的数据插补方法优于其他方法。这种新颖的工作流程适用于碎屑岩和碳酸盐岩储层。
为了在 3D 油藏建模中考虑油藏非均质性，需要采用先进技术来提高估算缺失岩心分析数据点的准确性和可靠性。缺失数据的填补是指一系列统计和机器学习 (ML) 方法，这些方法可补充岩心样品再分析或额外的岩心样品收集以填补数据空白。
本研究评估了涉及 ML 算法的七种数据插补方法：
这些岩心数据估算方法已应用于北鲁迈拉油田碎屑岩 Zubair 组的两口井。
The study described in the complete paper comprehensively compares seven imputation techniques for predicting missing core-measured horizontal and vertical permeability and porosity data in two wells drilled in the North Rumaila oil field of southern Iraq. The results reveal that a data-imputation method consisting of multivariate imputation by chained equations (MICE) combined with classification and regression trees (CART) outperforms the other methods with the clastic and carbonate data sets studied. The novel workflow is suitable for application in both clastic and carbonate reservoir formations.
To account for reservoir heterogeneity in 3D reservoir modeling, advanced techniques are required to improve the accuracy and reliability of estimating missing core-analysis data points. The imputation of missing data refers to a range of statistical and machine-learning (ML) methods that supplement core-sample reanalysis or additional core-sample collection to fill data gaps.
This study evaluates seven data-imputation methods involving ML algorithms:
These core-data imputation methods are applied to two wells penetrating the clastic Zubair formation of the North Rumaila oil field.
