摘要
通过提取数值模拟得到的流线场内质点的空间坐标及属性数据,建立流线簇流量、流线簇潜力和流线簇含油率的油藏流线场表征方法,应用密度峰值算法对流线场进行聚类分级评价,并通过SDbw系数验证划分流线等级的聚类效果,最终形成高含水期油藏流线场定量表征与评价的技术方法。结果表明,利用流线簇流量、流线簇潜力及流线簇含油率等3参数对流线场进行表征较常规方法更能反映注采井间流量及潜力的分布关系和大小,通过表征参数的聚类分级定量确定不同区域的流线强度等级。将流线场表征与评价方法应用于某东部油田实际区块,整个流线场被划分为14类,各区域驱替强度差异较大,通过流线场重构,调整前后流线场等级由14类变为7类,流动非均质性减弱,油藏动用程度明显改善。
By extracting the sp atial coordinates and attribute data of particles in streamline field obtained through numerical simulation,we established a characterization method of streamline field with streamline cluster flow rate,streamline cluster potential and streamline cluster oil content.The streamline field is classified and evaluated by density peak algorithm,and the clustering effect of streamline classification is verified by SDbwcoefficient.Finally the method of quantitative characterization and evaluation of streamline field in high water cut reservoir is formed.The results show that the three parameters can better reflect the distribution relationship and size of flow rate and potential between injection and production wells than conventional methods.Streamline strength grades in different regions can be quantitatively determined by clustering and grading of characterization parameters.The method is applied to an actual block in an eastern oilfield.The whole streamline field is divided into 14 types.The displacement intensity of each region is quite different.Through streamline field reconstruction,the streamline field grade are adjusted from 14 types to 7 types,the flow heterogeneity is weakened,and the reservoir productivity is improved obviously.
作者
柳朝阳
郭奇
李刚
黄博
王振宇
LIU Chaoyang;GUO Qi;LI Gang;HUANG Bo;WANG Zhenyu(Exploration and Development Technology Research Center,Yanchang Oilfield Co.Ltd.,Yan′an,Shaanxi 716000,China;Exploration and Development Research Institute,Shengli Oilfield Company,SINOPEC,Dongying,Shandong 257015,China;Post-doctoral Scientific Research Workstation of SINOPEC Shengli Petroleum Management Co.Ltd.,Dongying,Shandong 257000,China)
出处
《西南石油大学学报（自然科学版）》
CAS
CSCD
北大核心
2023年第1期89-96,共8页
Journal of Southwest Petroleum University(Science & Technology Edition)
基金
国家科技重大专项(2011ZX05011)
长江学者和创新团队发展计划(IRT1294)。
关键词
密度峰值算法
机器学习
流线场
均衡驱替
流线簇
density peak algorithm
machine learning
streamline field
equilibrium displacement
streamline cluster
