油气藏评价与开发 ›› 2025, Vol. 15 ›› Issue (4): 664-671.doi: 10.13809/j.cnki.cn32-1825/te.2025.04.016

• 矿场应用 • 上一篇    下一篇

低渗透油藏CO2驱开发全过程动态预测

王彦伟1(), 林利飞2, 王恒力3()   

  1. 1.中国石油集团川庆钻探工程有限公司长庆井下技术作业公司,陕西 西安 710021
    2.延长油田股份有限公司,陕西 延安 716000
    3.延安大学石油工程与环境工程学院,陕西 延安 716000
  • 收稿日期:2024-06-03 发布日期:2025-07-19 出版日期:2025-08-26
  • 通讯作者: 王恒力(1991—),男,博士,副教授,从事CO2驱提高采收率研究。地址:陕西省延安市宝塔区圣地路580号延安大学,邮政编码:716000。E-mail:wanghengli@yau.edu.cn
  • 作者简介:王彦伟(1984—),男,硕士,高级工程师,从事非常规油藏渗流规律研究。地址:陕西省西安市未央区兴隆园小区长庆大厦,邮政编码:710021。E-mail:1723981286@qq.com
  • 基金资助:
    国家自然科学基金项目“基于超声作用促进低渗透油藏CO2驱动态混相机理研究”(51974329)

Dynamic prediction of whole CO2 flooding development process in low permeability reservoirs

WANG Yanwei1(), LIN Lifei2, WANG Hengli3()   

  1. 1.CCDC Changqing Downhole Technology Company, Xi’an, Shaanxi 710021, China
    2.Yanchang Oil Field Co. , LTD. , Yan’an, Shaanxi 716000, China
    3.School of Petroleum Engineering and environment engineering Yan’an University, Yan’an, Shaanxi 716000, China
  • Received:2024-06-03 Online:2025-07-19 Published:2025-08-26

摘要:

CO2驱能有效提高低渗透油藏采收率,但由于低渗透油藏普遍存在强非均质性,导致CO2驱开发动态难以准确预测。针对该问题,在综合考虑喉道大小及分布、CO2溶解降黏和界面张力变化等因素的基础上,结合CO2驱渗流力学理论,建立了基于时间节点的低渗透油藏CO2驱开发全过程动态预测模型。该模型创新性地实现了考虑油藏微观非均质性的全过程动态预测。结果表明:喉道半径对CO2驱替初期的渗流阻力影响较大,同时CO2驱替过程中伴随的扩散-溶解-降黏-降阻的作用不断迭代耦合,导致同一时刻不同半径的喉道中CO2驱替前缘位置不同。这种差异反映在开发动态上表现为:储层孔喉半径越大、物性越好;油井见气时间越早,同一时刻油井的气油比越高。根据注采井间CO2体积分数分布,可将驱替过程划分为纯CO2区、传质扩散区和纯油区3个区域。当大喉道传质扩散区前缘到达采油井时油井开始见气,油井产量也逐渐增大,此后采出程度迅速增加;纯CO2区前缘到达采油井时气油比迅速增加,油井产量迅速减小,采出程度曲线增幅减小直至趋于平稳。对比实验结果:模型预测采收率误差分别为5.7%和4.5%,气油比及采出程度曲线均比较吻合。运用该方法预测了H3试验区的开发动态,对分析CO2驱开发动态、及时调整气窜井开发制度起到了关键指导作用。

关键词: 低渗透油藏, CO2驱, 溶解降黏, 开发动态预测, 采出程度

Abstract:

CO2 flooding can effectively enhance oil recovery of low permeability reservoirs. However, due to the common presence of strong heterogeneity in such reservoirs, accurately predicting the development dynamics of CO2 flooding is difficult. To address this issue, a time-node-based dynamic prediction model for the entire CO2 flooding process in low permeability reservoirs was developed, based on the comprehensive consideration of factors such as throat size and distribution, viscosity reduction due to CO2 dissolution, and changes in interfacial tension, combined with CO2 flooding seepage mechanics theory. This model achieved innovative whole-process dynamic prediction by accounting for reservoir micro-heterogeneity. The results showed that the throat radius significantly influences flow resistance during the early stage of CO2 displacement. Meanwhile, the continuous iterative coupling of diffusion, dissolution, viscosity reduction, and drag reduction during the CO2 displacement process led to differences in the displacement front positions in throats of different radii at the same time. The difference was reflected in development dynamics: larger pore-throat radius and better reservoir properties led to earlier gas breakthrough and higher gas-oil ratio at the same time. According to the CO2 volume fraction distribution between injection and production wells, the displacement process could be divided into three zones: pure CO2 zone, mass transfer diffusion zone, and pure oil zone. When the front of the mass transfer diffusion zone in large throats reached the production well, gas breakthrough occurred, and oil production gradually increased; thereafter, the oil recovery increased rapidly. When the front of the pure CO2 zone reached the production well, the gas-oil ratio increased rapidly, oil production decreased sharply, and the growth rate of the recovery curve slowed down and eventually stabilized. Compared with the experimental results, the predicted recovery errors of the model were 5.7% and 4.5%, respectively, with good agreement in gas-oil ratio and oil recovery curves. This method was used to predict the development dynamics of the H3 experimental area, providing critical guidance for analyzing CO2 flooding performance and timely adjustment of the development strategy for gas channeling wells.

Key words: low permeability reservoir, CO2 flooding, viscosity reduction due to dissolution, development dynamic prediction, oil recovery

中图分类号: 

  • TE341