摘要
为建立一套海上砂岩油藏定向井初期产能的预测方法,综合考虑地质、工程与开发3个方面17项初期产能的影响因素,基于45口海上砂岩油藏定向井的2700组数据,组合使用Spearman相关系数、随机森林与递归特征消除算法对影响因素进行重要性排序;并结合油藏工程逻辑判别,筛选初期产能的主控因素。选用极端梯度提升(XG⁃Boost)算法构建初期产能预测模型,并基于产能公式改进其损失函数,增强数据挖掘算法的物理约束。结果表明:地层流动系数、孔隙度、层间地层流动系数变异系数、电潜泵下入垂深、储层射开厚度、井眼尺寸、生产压差、电潜泵频率以及油嘴尺寸是海上砂岩油藏定向井初期产能的主控因素。采用物理约束的XGBoost算法对5口井初期产能预测的平均相对误差为9.68%,而无物理约束的XGBoost算法预测初期产能的平均相对误差为11.68%。因此,物理约束可有效提升XGBoost算法对初期产能的预测精度,同时可实现海上砂岩油藏定向井初期产能的准确预测。
This research aims to establish a prediction method for the initial productivity of directional wells in offshore sandstone oil reservoirs.A total of 17 factors affecting the initial productivity were considered from aspects of the geology,engineering,and development.Given 2700 sets of data from 45 directional wells in offshore sandstone oil reservoirs,the Spearman correlation coefficient,random forest,and recursive feature elimination algorithm were combined to rank the importance of these influencing factors.With the logic of reservoir engineering,the main controlling factors in the initial productivity were selected.On this basis,the initial productivity prediction model was constructed by the extreme gradient boosting(XGBoost)algorithm,and its loss function was improved by referring to the productivity formula to enhance its physical constraints of this data mining algorithm.The results show that the main controlling factors affecting initial productivity of directional wells in offshore sandstone oil reservoirs include the formation flow coefficient,porosity,variation coefficient of the formation flow coefficient between layers,vertical depth of an electric submersible pump(ESP),perforation thickness of reservoirs,borehole size,drawdown,frequency of ESP,and choke size.Furthermore,the average relative error of the XGBoost algorithm with physical constraints for predicting the initial productivity of five wells is 9.68%,and the average relative error of the XGBoost algorithm without physical constraints is 11.68%.Therefore,physical constraints can effectively improve the accuracy of the XGBoost algorithm in productivity prediction,and the proposed method can realize the accurate prediction of the initial productivity of directional wells in offshore sandstone oil reservoirs.
作者
董银涛
宋来明
张迎春
邱凌
余洋
卢川
DONG Yintao;SONG Laiming;ZHANG Yingchun;QIU Ling;YU Yang;LU Chuan(CNOOC Research Institute Co.,Ltd.,Beijing City,100028,China;China United Coalbed Methane Co.,Ltd.,Beijing City,100026,China;PetroChina Research Institute of Petroleum Exploration and Development,Beijing City,100083,China)
出处
《油气地质与采收率》
CAS
CSCD
北大核心
2022年第1期137-144,共8页
Petroleum Geology and Recovery Efficiency
基金
中海油研究总院有限责任公司博士后基金项目“海上砂岩油藏产能数据挖掘方法研究”(2020-KFZL-015)
中海石油(中国)有限公司科技课题“地质油藏大数据深度挖掘及应用研究”(2020-YXKJ-016)。
关键词
定向井
初期产能
主控因素
XGBoost
产能公式
directional well
initial productivity
main controlling factor
XGBoost
productivity formula
