石油勘探与开发 >

2021 , Vol. 48 >Issue 4: 787 - 796

DOI: https://doi.org/10.11698/PED.2021.04.11

油气田开发

基于三维分形裂缝模型的页岩气井智能化产能评价方法

  • 位云生 ,
  • 王军磊 ,
  • 于伟 ,
  • 齐亚东 ,
  • 苗继军 ,
  • 袁贺 ,
  • 刘楚溪
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  • 1.中国石油勘探开发研究院,北京 100083;
    2.德克萨斯大学奥斯汀分校,德克萨斯 78712,美国;
    3.SimTech有限责任公司,休斯顿 77494,美国
位云生（1979-）,男,河南项城人,博士,中国石油勘探开发研究院高级工程师,主要从事气田开发综合研究工作。地址：北京市海淀区学院路20号,中国石油勘探开发研究院气田开发研究所,邮政编码：100083。E-mail: weiys@petrochina.com.cn

收稿日期: 2020-11-23

  修回日期: 2021-05-07

  网络出版日期: 2021-07-23

基金资助

国家科技重大专项“昭通页岩气勘探开发示范工程”（2017ZX05063-005）; 中国石油勘探开发研究院科技开发项目“复杂缝网条件下页岩气井生产动态数值模拟程序开发”（YGJ2019-12-04）

收起

A smart productivity evaluation method for shale gas wells based on 3D fractal fracture network model

  • WEI Yunsheng ,
  • WANG Junlei ,
  • YU Wei ,
  • QI Yadong ,
  • MIAO Jijun ,
  • YUAN He ,
  • LIU Chuxi
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  • 1. Research Institute of Petroleum Exploration & Development, PetroChina, Beijing 100083, China;
    2. University of Texas at Austin, Austin TX 78712, USA;
    3. SimTech LLC, Houston TX 77494, USA

Received date: 2020-11-23

  Revised date: 2021-05-07

  Online published: 2021-07-23

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摘要

基于分形理论多次信息叠加算法设计了三维天然裂缝生成方法,人工裂缝模型与天然裂缝模型耦合可表征页岩压后复杂多尺度裂缝系统。采用马尔科夫链-蒙特卡洛智能化历史拟合算法,结合嵌入式离散裂缝技术耦合数值模拟器预测页岩气井产能,形成了一体化页岩气井产能评价模型。研究表明：三维天然裂缝生成方法可利用分形参数控制裂缝网络的整体分布,与人工裂缝耦合可表征页岩压后复杂的跨尺度裂缝系统;嵌入式离散裂缝模型具有裂缝网格数量少、运算耗时短的优点,能够灵活表征天然裂缝及人工裂缝属性,在有效降低计算量的同时能够精确地模拟流体在基质-裂缝中的交换过程;嵌入式离散裂缝模型与智能化历史拟合算法相结合能够降低裂缝、储集层等未知参数计算的不确定性,实现储集层、裂缝关键参数的有效反演,并实现气井产能量化预测。经实际应用验证一体化井产能评价模型预测结果具有较高的可信度。 图13 表7 参26

关键词： 分形裂缝网络; 多次信息叠加算法; 嵌入式裂缝模型; 智能化历史拟合; 储集层参数反演; 页岩气; 智能化产能评价

本文引用格式

位云生 , 王军磊 , 于伟 , 齐亚东 , 苗继军 , 袁贺 , 刘楚溪 . 基于三维分形裂缝模型的页岩气井智能化产能评价方法[J]. 石油勘探与开发, 2021 , 48(4) : 787 -796 . DOI: 10.11698/PED.2021.04.11

Abstract

The generation method of three-dimensional fractal discrete fracture network (FDFN) based on multiplicative cascade process was developed. The complex multi-scale fracture system in shale after fracturing was characterized by coupling the artificial fracture model and the natural fracture model. Based on an assisted history matching (AHM) using multiple-proxy-based Markov chain Monte Carlo algorithm (MCMC), an embedded discrete fracture modeling (EDFM) incorporated with reservoir simulator was used to predict productivity of shale gas well. When using the natural fracture generation method, the distribution of natural fracture network can be controlled by fractal parameters, and the natural fracture network generated coupling with artificial fractures can characterize the complex system of different- scale fractures in shale after fracturing. The EDFM, with fewer grids and less computation time consumption, can characterize the attributes of natural fractures and artificial fractures flexibly, and simulate the details of mass transfer between matrix cells and fractures while reducing computation significantly. The combination of AMH and EDFM can lower the uncertainty of reservoir and fracture parameters, and realize effective inversion of key reservoir and fracture parameters and the productivity forecast of shale gas wells. Application demonstrates the results from the proposed productivity prediction model integrating FDFN, EDFM and AHM have high credibility.

Key words： fractal discrete fracture network; multiplicative cascade process; embedded discrete fracture model; intelligent history matching; reservoir parameter inversion; shale gas; smart productivity evaluation

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