摘要
在一些多层底水油藏注水开发中,由于油水关系复杂、纵向各层物性及流体性质差异大,因此层间矛盾突出,后期调整难度较大。以秦皇岛32-6油田为例,采用动态反演方法定量评价层间干扰变化规律。综合考虑油水关系、渗透率、有效厚度及流体等关键因素,建立了层间干扰定量预测方法;综合考虑层间干扰与启动压力的影响,建立了普通稠油油藏多层合采定向井产能预测方法。研究发现,底水油藏在合采开发中易发生锥进现象而使层间干扰进一步加剧,严重影响整体开发效果。由于层间干扰现象随着含水率的上升而加剧,因此可将中低含水期流动系数级差10作为划分层系的界限,并建议在高含水期进行层系重组。
For multi-layer sandstone reservoirs with complex oil-water relationship and large differences in physical properties and fluid properties of each layer in the vertical direction,the contradictions between layers are prominent during the water injection development process,and it is difficult to divide the hierarchy and adjust later.Taking QHD32-6 as an example,the dynamic inversion method is used to quantitatively evaluate the inter-layer interference changing law.The key factors such as oil-water relationship,permeability,effective thickness and fluid are comprehensively considered to establish a prediction method of inter-layer interference;considering the influence of interlayer interference and start-up pressure,the productivity prediction method of multi-layer combined production directional well in common heavy oil reservoir is established.The following conclusions have come into being:when the bottom water reservoir participates in combined production and development,coning is easy to occur,which obviously aggravates the interlayer interference and seriously affects the overall development effect.As the water cut rises,the interlayer interference intensifies,so the flow coefficient difference of 10 can be used to divide the boundaries of the strata system,and it is recommended to reorganize the strata system during the high water cut period.
作者
康博韬
姜彬
陈国宁
李晨曦
郜益华
KANG Botao;JIANG Bin;CHEN Guoning;LI Chenxi;GAO Yihua(CNOOC Research Institute Co.Ltd.,Beijing 100028,China)
出处
《重庆科技学院学报（自然科学版）》
CAS
2021年第4期18-23,共6页
Journal of Chongqing University of Science and Technology：Natural Sciences Edition
基金
国家科技重大专项“海上油田丛式井网整体加密及综合调整技术”(2011ZX05024-002)。
关键词
多层合采
油水系统
层间干扰
流动系数
产能评价
multi-layer production
oil-water system
interlayer interference
flow coefficient
productivity prediction
