方法理论

注水井优化配注方法应用现状及发展方向

  • 罗宪波 ,
  • 常会江 ,
  • 雷源 ,
  • 翟上奇 ,
  • 孙广义
展开
  • 中海石油(中国)有限公司天津分公司渤海石油研究院,天津 300459
罗宪波(1975— ),男,博士,教授级高级工程师,主要从事油气田开发工程方面的研究。地址:天津市滨海新区海川路2121号海洋石油大厦B座,邮政编码:300459。E-mail:luoxb@cnooc.com.cn

收稿日期: 2021-04-15

  网络出版日期: 2023-04-26

基金资助

国家科技重大专项“渤海油田加密调整油藏工程技术研究”(2016ZX05025-001-007)

Application status and development direction of optimal injection allocation method for water injection wells

  • Xianbo LUO ,
  • Huijiang CHANG ,
  • Yuan LEI ,
  • Shangqi ZHAI ,
  • Guangyi SUN
Expand
  • Bohai Petroleum Research Institute,CNOOC China Tianjin Branch, Tianjin 300459, China

Received date: 2021-04-15

  Online published: 2023-04-26

摘要

针对现有注水井优化配注方法缺乏统一性和规范性的问题,以注水井优化配注涉及的关键参数纵向劈分系数、平面劈分系数和注采比3个方面为切入点,对国内外学者在注水井优化配注方面的研究进行总结和归纳,得到了适用于不同场合下的注水井优化配注计算方法。在此基础上以采油井为中心,提出了注水井优化配注新方法。新方法重点强调了注水井的优化配注应根据不同地质油藏特征、油田开发阶段、配注精度需求等因素选择不同的优化配注参数计算方法,从而使油田优化注水效果最佳。并将优化配注新思路在矿场进行试验,实现区块日增油幅度高达10 %(日增油20 m3)的良好开发效果。最后指出了未来优化注水方向必然是基于人工智能的注水方案设计、智能优化和同步调整为核心的油藏和采油工程一体化。以上注水井优化配注方法,对不同类型不同开发阶段油田的注水工作具有指导意义。

本文引用格式

罗宪波 , 常会江 , 雷源 , 翟上奇 , 孙广义 . 注水井优化配注方法应用现状及发展方向[J]. 油气藏评价与开发, 2023 , 13(2) : 223 -232 . DOI: 10.13809/j.cnki.cn32-1825/te.2023.02.011

Abstract

There is a lack of uniformity and standardization of the existing optimal injection allocation method. Therefore, three key parameters involved in the optimization of water injection well allocation, which are longitudinal splitting coefficient, plane splitting coefficient and injection-production ratio, are taking as the breakthrough point. And then, the research on the allocation optimization of injection wells by the scholars at home and abroad is summarized. Finally, the calculation method of injection well allocation suitable for different occasions is obtained. On this basis, a new idea of optimizing injection allocation for water injection wells is proposed with the oil production wells as the center. This new idea emphasizes that for the optimal injection allocation of the water injection wells, the optimal injection parameter calculation method should be selected according to different geological reservoir characteristics, oilfield development stages, injection accuracy and other factors, so that to make the oilfield optimized water injection effect best. After this method is applied to the field test, a good development effect is achieved with a daily oil increase of up to 10 %(daily oil increase of 20 m3) in the block. In general, the optimization of water injection direction in the future must be the integration of reservoir and oil production engineering with the water injection scheme design, intelligent optimization and synchronous adjustment are the core based on artificial intelligence. The proposed optimized injection well allocation method has guiding significance for the water injection work of different types of oilfields at different stages of development.

参考文献

[1] 黄昌武. 2012年中国石油十大科技进展[J]. 石油勘探与开发, 2013, 40(2): 208.
[1] HUANG Changwu. Top ten scientific and technological progress of PetroChina in 2012[J]. Petroleum Exploration and Development, 2013, 40(2): 208.
[2] 刘合, 裴晓含, 罗凯, 等. 中国油气田开发分层注水工艺技术现状与发展趋势[J]. 石油勘探与开发, 2013, 40(6): 733-737.
[2] LIU He, PEI Xiaohan, LUO kai, et al. Current status and trend of separated layer water flooding in China[J]. Petroleum Exploration and Development, 2013, 40(6): 733-737.
[3] 王桐, 金心岫, 陈雅彤. 青平川油区长2油藏水驱采收率计算及评价[J]. 石油地质与工程, 2022, 36(6): 67-71.
[3] WANG Tong, JIN Xinxiu, CHEN Yatong. Water drive recovery calculation[J]. Petroleum Geology & Engineering, 2022, 36(6): 67-71.
[4] 谭文斌. 油田注水开发的决策部署研究[M]. 北京: 石油工业出版社, 2000.
[4] TAN Wenbin. Study on decision-making and deployment of oilfield water injection development[M]. Beijing: Petroleum Industry Press, 2000.
[5] 张继成, 王潇悦. 考虑含水饱和度的产量劈分方法及应用[J]. 浙江大学学报(理学版), 2015, 42(5): 626-630.
[5] ZHANG Jicheng, WANG Xiaoyue. The method of oil production splitting based on water saturation and its application[J]. Journal of Zhejiang University(Science Edition), 2015, 42(5): 626-630.
[6] 贾晓飞, 李其正, 杨静, 等. 基于剩余油分布的分层调配注水井注入量的方法[J]. 中国海上油气, 2012, 24(3): 38-40.
[6] JIA Xiaofei, LI Qizheng, YANG Jing, et al. A method to allocate injection volume for separate layers in a water-injection well based on the remaining oil distribution[J]. China Offshore Oil and Gas, 2012, 24(3): 38-40.
[7] 孙召勃, 李云鹏, 贾晓飞, 等. 基于驱替定量表征的高含水油田注水井分层配注量确定方法[J]. 石油钻探技术, 2018, 46(2): 87-91.
[7] SUN Zhaobo, LI Yunpeng, JIA Xiaofei, et al. A method to determine the layered injection allocation rates for water injection wells in high water cut oilfield based on displacement quantitative Characterization[J]. Petroleum Drilling Techniques, 2018, 46(2): 87-91.
[8] 杜庆龙, 朱丽红. 油、水井分层动用状况研究新方法[J]. 石油勘探与开发, 2004, 31(5): 96-98.
[8] DU Qinglong, ZHU Lihong. A new approach to study layered producing performance of oil and water wells[J]. Petroleum Exploration and Development, 2004, 31(5): 96-98.
[9] 陈建华, 晏庆辉, 骆逸婷, 等. 基于历史生产数据的多层合采井产量劈分新方法[J]. 中国海上油气, 2022, 34(1): 110-116.
[9] CHEN Jianhua, YAN Qinghui, LUO Yiting, et al. A historical production data based method for production splitting of multi-layer commingled gas wells[J]. China Offshore Oil and Gas, 2022, 34(1): 110-116.
[10] YOUSEF A A, GENTIL P H, Jensen J L, et al. A capacitance model to infer interwell connectivity from production and injection rate fluctuations[J]. SPE Reservoir Evaluation & Engineering, 2006, 9(6): 630-646.
[11] JAMALI Ali, ETTEHADTAVAKKOL Amin. 应用电容电阻模型研究大型成熟油田井间连通性[J]. 石油勘探与开发, 2017, 44(1): 130-136.
[11] JAMALI Ali, ETTEHADTAVAKKOL Amin. Application of capacitance resistance models to interwell connectivity of large-scale mature oil fields[J]. Petroleum Exploration and Development, 2017, 44(1): 130-136.
[12] ZHAO H, KANG Z J, ZHANG X S, et al. A physics-based data-driven numerical model for reservoir history matching and prediction with a field application[J]. SPE Journal, 2016, 21(6): 2175-2194.
[13] 赵辉, 康志江, 孙海涛, 等. 水驱开发多层油藏井间连通性反演模型[J]. 石油勘探与开发, 2016, 43(1): 99-106.
[13] ZHAO Hui, KANG Zhijiang, SUN Haitao, et al. An interwell connectivity inversion model for waterflooded multilayer reservoirs[J]. Petroleum Exploration and Development, 2016, 43(1): 99-106.
[14] 孙致学, 黄勇, 王业飞, 等. 基于流线模拟的水井配注量优化方法[J]. 断块油气田, 2016, 23(6): 753-757.
[14] SUN Zhixue, HUANG Yong, WANG Yefei, et al. Optimization of water injection allocation based on streamline simulation[J]. Fault-Block Oil & Gas Field, 2016, 23(6): 753-757.
[15] 黄勇, 王业飞, 孙致学, 等. 基于流线模拟的高含水油田注水效率优化[J]. 西安石油大学学报(自然科学版), 2017, 32(2): 53-58.
[15] HUANG Yong, WANG Yefei, SUN Zhixue, et al. Optimization of water injection efficiency based on streamline simulation in high water cut stage[J]. Journal of Xi'an Shiyou University(Natural Science Edition), 2017, 32(2): 53-58.
[16] BLUT M J, LIU K R, THILE M R. A generalized streamline method to predict reservoir flow[J]. Petroleum Geosciences, 1996, 2: 259-269.
[17] 张俊, 黄琴, 杨静, 等. 海上半衰竭式水驱开发稠油油藏地层压力恢复研究与应用[J]. 石油地质与工程, 2013, 27(6): 127-129.
[17] ZHANG Jun, HUANG Qin, YANG Jing, et al. Research and application of formation pressure recovery in offshore semi-depleted waterflooding development of heavy oil reservoirs[J]. Petroleum Geology & Engineering, 2013, 27(6): 127-129.
[18] 那雪芳, 姚尚空, 孙晨曦. 大庆油田中区西部合理注采比的确定[J]. 北京石油化工学院学报, 2019, 27(2): 44-48.
[18] NA Xuefang, YAO Shangkong, SUN Chenxi. Study of the reasonable injection-production ratio in the west part of central block in saertu of Daqing Oilfield[J]. Journal of Beijing Institute of Petrochemical Technology, 2019, 27(2): 44-48.
[19] 林伯韬, 郭建成. 人工智能在石油工业中的应用现状探讨[J]. 石油科学通报, 2019, 4(4): 403-413.
[19] LIN Botao, GUO Jiancheng. Discussion on current application of artificial intelligence in petroleum industry[J]. Petroleum Science Bulletin, 2019, 4(4): 403-413.
[20] 李阳, 廉培庆, 薛兆杰, 等. 大数据及人工智能在油气田开发中的应用现状及展望[J]. 中国石油大学学报(自然科学版), 2020, 44(4): 1-11.
[20] LI Yang, LIAN Peiqing, XUE Zhaojie, et al. Application status and prospect of big data and artificial intelligence in oil and gas field development[J]. Journal of China University of Petroleum(Natural Science Edition), 2020, 44(4): 1-11.
[21] 张凯, 赵兴刚, 张黎明, 等. 智能油田开发中的大数据及智能优化理论和方法研究现状及展望[J]. 中国石油大学学报(自然科学版), 2020, 44(4): 28-38.
[21] ZHANG Kai, ZHAO Xinggang, ZHANG Liming, et al. Current status and prospect for the research and application of big data and intelligent optimization methods in oilfield development[J]. Journal of China University of Petroleum(Natural Science Edition), 2020, 44(4): 28-38.
[22] 冯硕, 张艺耀, 李进, 等. 渤海油田远程无线智能注水工艺技术及应用[J]. 石油机械, 2021, 49(11): 79-83.
[22] FENG Shuo, ZHANG Yiyao, LI Jin, et al. Remote Wireless Intelligent Water Injection Technology and its Application in Bohai Oilfield[J]. China Petroleum Machinery, 2021, 49(11): 79-83.
[23] 叶勤友, 刘亚珍, 孙伟, 等. 智能化多管分层注水技术研究与应用[J]. 石油机械, 2021, 49(6): 82-87.
[23] YE Qinyou, LIU Yazhen, SUN Wei, et al. Research of Intelligent Multi-pipe Separate Zone Injection Technology[J]. China Petroleum Machinery, 2021, 49(6): 82-87.
[24] 贾德利, 刘合, 张吉群, 等. 大数据驱动下的老油田精细注水优化方法[J]. 石油勘探与开发, 2020, 47(3): 629-636.
[24] JIA Deli, LIU He, ZHANG Jiqun, et al. Data-driven optimization for fine water injection in a mature oil field[J]. Petroleum Exploration and Development, 2020, 47(3): 629-636.
文章导航

/