Special Oil & Gas Reservoirs >

2021 , Vol. 28 >Issue 3: 99 - 105

DOI: https://doi.org/10.3969/j.issn.1006-6535.2021.03.015

Reservoir Engineering

Study on Automatic Reservoir History Matching Based on ES-MDA Algorithm

  • Wang Zelong ,
  • Liu Xiangui ,
  • Tang Haifa ,
  • Lyu Zhikai ,
  • Liu Qunming
Expand
  • 1. University of Chinese Academy of Sciences, Beijing 100049, China;
    2. Institute of Porous Flow and Fluid Mechanics, University of Chinese Academy of Sciences, Langfang, Hebei 065007, China;
    3. China National Oil and Gas Exploration and Development Co., Ltd., Beijing 100034, China;
    4. Research Institute of Petroleum Exploration & Development, Beijing 100083, China

Received date: 2020-12-28

  Revised date: 2021-03-08

  Online published: 2022-02-16

Fold

Abstract

To address the shortcomings of common history matching methods such as large amounts of computation, abnormal update of reservoir parameters, and distortion of reservoir model corrections, ensemble smoother algorithm was adopted to repeatedly assimilate the same data by multiple iterations in the ensemble Kalman filter (EnKF) algorithm to derive the core formula of the ES-MDA algorithm (ensemble smoother with multiple data assimilations) and write automatic reservoir history matching software. In a case study of marine sandstone reservoir of Brent Oilfield in the North Sea, the automatic history matching program of the reservoir based on the ES-MDA was applied to the reservoir to conduct history matching for water injection, oil production and development of the oilfield. The results showed that the predicted data obtained from the numerical modeling of the reservoir matched the actual measured data by more than 90%, and characterized the porosity distribution of the real reservoir more accurately. The ES-MDA algorithm is advantaged by stable algorithm, high operation efficiency and accurate model updating. The research results are of great significance for realizing computer-based automatic reservoir history matching and real-time optimization of reservoir production.

Key words： history matching; mathematical model; simulation algorithm; data assimilation; ensemble smoother; ensemble Kalman filter

Cite this article

Wang Zelong , Liu Xiangui , Tang Haifa , Lyu Zhikai , Liu Qunming . Study on Automatic Reservoir History Matching Based on ES-MDA Algorithm[J]. Special Oil & Gas Reservoirs, 2021 , 28(3) : 99 -105 . DOI: 10.3969/j.issn.1006-6535.2021.03.015

References

[1] WANG Zelong,LIU Xiangui,TANG Haifa,et al.Geophysical and production data history matching based on ensemble smoother with multiple data assimilation[J].Computer Modeling in Engineering & Sciences,2020,123(2):873-893.
[2] 闫霞.基于梯度逼真算法的油藏生产优化理论研究[D].青岛:中国石油大学(华东),2013.
YAN Xia.Theoretical study on reservoir production optimization based on gradient fidelity algorithm[D].Qingdao:China University of Petroleum(East China),2013.
[3] 赵辉,唐乙玮,康志江,等.油藏开发生产优化近似扰动梯度升级算法[J].中国石油大学学报(自然科学版),2016,40(2):99-104.
ZHAO Hui,TANG Yiwei,KANG Zhijiang,et al.Reservoir production optimization using an upgraded perturbation gradient approximation algorithm[J].Journal of China University of Petroleum(Edition of Natural Sciences),2016,40(2):99-104.
[4] 张楚楚.基于贝叶斯的反问题计算[D].杭州:浙江大学,2018.
ZHANG Chuchu.The Bayesian solution of inverse problems[D].Hangzhou:Zhejiang University,2018.
[5] 马建文,秦思娴.数据同化算法研究现状综述[J].地球科学进展,2012,27(7):747-757.
MA Jianwen,QIN Sixian.Recent advances and development of data assimilation algorithms[J].Advances in Earth Science,2012,27(7):747-757.
[6] EVENSEN G.Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics[J].Journal of Geophysical Research,1994,99(C5):10143-10162.
[7] NÆVDAL G,MANNSETH T,VEFRING E H.Near-well reservoir monitoring through ensemble Kalman filter[C].SPE75235-MS,2002:13-17.
[8] LORENTZEN R J,NÆVDAL G,VALLES B,et al.Analysis of the ensemble Kalman filter for estimation of permeability and porosity in reservoir models[C].SPE96375-MS,2005:1-8.
[9] GU Y,OLIVER D S.History matching of the PUNQ-S3 reservoir model using the ensemble Kalman filter[J].SPE Journal,2005,10(2):51-65.
[10] CHEN Y,OLIVER D S,ZHANG D.Efficient ensemble-based closed-loop production optimization[J].SPE Journal,2009,14(4):634-645.
[11] LI R,REYNOLDS A C,OLIVER D S.Sensitivity coefficients for three-phase flow history matching[J].Canada Petroleum Technology.2003,42(4):70-77.
[12] JAFARPOUR B,MCLAUGHLIN D B.History matching with an ensemble Kalman filter and discrete cosine parameterization[J].Computational Geosciences,2008,12(2):227-244.
[13] 王玉斗,何健,李高明.基于离散余弦变换的集合Kalman滤波方法对河流相油藏渗透率场的反演[J].地球物理学报,2011,54(7):1900-1911.
WANG Yudou,HE Jian,LI Gaoming.Inverse of the fluvial channel reservoir permeability field using ensemble Kalman filter based on discrete cosine transform[J].Chinese Journal of Geophysics,2011,54(7):1900-1911.
[14] 薛亮,吴雨娟,刘倩君,等.裂缝性油气藏数值模拟与自动历史拟合研究进展[J].石油科学通报,2019,4(4):335-346.
XUE Liang,WU Yujuan,LIU Qianjun,et al.Advances in numerical simulation and automatic history matching of fractured reservoirs[J].Petroleum Science Bulletin,2019,4(4):335-346.
[15] EMERICK A A,REYNOLDS A C.History matching time-lapse seismic data using the ensemble Kalman filter with multiple data assimilations[J].Computational Geosciences,2012,16:639-659.
[16] EMERICK A A,REYNOLDS A C.Ensemble smoother with multiple data assimilation[J].Computers & Geosciences,2013,55:3-15.
[17] LEEUWENBURGH O,ARTSR.Distance parameterization for efficient seismic history matching with the ensemble Kalman Filter[J].Computational Geosciences,2014,18(3/4):535-548.
Options
Outlines

/

〈 〉