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强底水礁灰岩油藏水驱采收率表征模型
引用本文:罗东红,朱旭,戴宗,程佳,宁玉萍. 强底水礁灰岩油藏水驱采收率表征模型[J]. 西南石油大学学报(自然科学版), 2018, 40(5): 105-112. DOI: 10.11885/j.issn.1674-5086.2017.11.07.01
作者姓名:罗东红  朱旭  戴宗  程佳  宁玉萍
作者单位:1. 中海石油(中国)有限公司深圳分公司, 广东 深圳 518067;2. 西南石油大学石油与天然气工程学院, 四川 成都 610500
基金项目:中海油科技重大专项(YXKY-2015-SZ-01)
摘    要:为了对海上强底水礁灰岩L油藏水驱采收率进行快速预测,通过对油藏开发过程中的地质参数、流体参数和工程参数进行分析总结,建立了包含影响开发效果各个因素的机理模型,并结合数值模拟研究,获取了采收率与各影响因素间的函数关系,确定了影响油藏水驱采收率的主控因素。在此基础上,运用正交实验设计与多元非线性回归理论,建立了礁灰岩油藏水平井开发水驱采收率定量表征模型。实例应用结果表明,该采收率表征模型可用于强底水水平井开发条件下的礁灰岩油藏采收率快速预测,预测结果精度较高,误差小。

关 键 词:礁灰岩  采收率  水驱  影响因素  定量表征模型  
收稿时间:2017-11-07

Recovery Rate Model for Strong Bottom Water Drive Reef Limestone Reservoirs
LUO Donghong,ZHU Xu,DAI Zong,CHENG Jia,NING Yuping. Recovery Rate Model for Strong Bottom Water Drive Reef Limestone Reservoirs[J]. Journal of Southwest Petroleum University(Seience & Technology Edition), 2018, 40(5): 105-112. DOI: 10.11885/j.issn.1674-5086.2017.11.07.01
Authors:LUO Donghong  ZHU Xu  DAI Zong  CHENG Jia  NING Yuping
Affiliation:1. CNOOC China Limited, Shenzhen Branch, Shenzhen, Guangdong 518067, China;2. School of Oil & Natural Gas Engineering, Southwest Petroleum University, Chengdu, Sichuan 610500, China
Abstract:To realize rapid prediction of the recovery rate of offshore strong bottom water drive reef limestone reservoir L, a model for simulating the mechanisms of the various factors affecting the reservoir exploitation was developed. The model was developed by reviewing and analyzing the geological, fluid, and engineering parameters associated with the reservoir exploitation process. The functional relationships between the recovery rate and the various influencing factors were then determined on the basis of the model as well as a numerical simulation study, thereby yielding a list of the major factors affecting the recovery rate of the water drive reservoir. Through this approach, a model was then developed for quantitatively representing the recovery rate of water drive reef limestone reservoirs, by employing an orthogonal experiment design and multiple nonlinear regression theory. Field application results showed that the recovery rate model was capable of rapidly predicting the recovery rate of strong bottom water drive reef limestone reservoirs exploited using horizontal wells, with high accuracy and small error.
Keywords:reef limestones  recovery rates  waterflooding  influencing factor  quantitative characterization model  
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