摘要
【目的】为了实现压裂参数的快速和精准设优化。【方法】本研究利用大数据技术及多种机器学习算法,采用基于数据模型的压裂参数智能优化方法来获得最优压裂施工参数组合。【结果】本研究以苏东南某致密气井区的历史压裂数据为基础,利用上述方法对该区的74口水平井的压裂总液量、加砂强度、砂比进行优化设计,并预测压后产能。优化结果表明,该区合理的总液量为3000~4600 m^(3),加砂强度优化为2~3,砂比优化为17~19,优化后增气量为1890~3555万m^(3),压裂参数优化效果显著。【结论】该方法能有效提高压后产能,为其他非常规油气田压裂参数优化设计提供参考。
[Purposes]In order to realize the fast and accurate optimization of fracturing parameters.[Methods]The optimal fracturing operation parameter combination is obtained through the intelligent optimization method of fracturing parameters based on data model by using big data technology and various machine learning algorithms.[Findings]Based on the historical fracturing data of a tight gas well area in southeastern Jiangsu,this method was used to optimize the design of the total fracturing fluid volume,sand filling strength,and sand ratio of 74 horizontal wells in the area,and to predict the post-fracturing productivity.The optimization results show that the reasonable total liquid volume in this area is between 3000~4600 cubic meters,the sand adding strength is optimized between 2~3,the sand ratio is optimized between 17~19,and the optimized gas increase is 18.9~35.55 million cubic meters,the fracturing parameter optimization effect is remarkable.[Conclusions]This method effectively improves the postfracturing productivity,and can provide method basis and technical reference for the optimization design of fracturing parameters in other unconventional oil and gas fields.
作者
陈德承
CHEN Decheng(Jingzhou Institute of Technology,Jingzhou 434000,China)
出处
《河南科技》
2023年第1期23-27,共5页
Henan Science and Technology
基金
非常规油气省部共建协同创新中心开放基金项目(UOG2022-36)。
关键词
大数据技术
人工智能
数据代理模型
粒子群优化
压裂参数优化
big data technology
artificial intelligence
data proxy model
particle swarm optimization
fracturing parameter optimization
