Review of Field Development Optimization of Waterflooding, EOR, and Well Placement Focusing on History Matching and Optimization Algorithms

被引:39
|
作者
Udy, Jackson [1 ]
Hansen, Brigham [1 ]
Maddux, Sage [1 ]
Petersen, Donald [1 ]
Heilner, Spencer [1 ]
Stevens, Kevin [1 ]
Lignell, David [1 ]
Hedengren, John D. [1 ]
机构
[1] Brigham Young Univ, Ira A Fulton Coll Engn & Technol, Dept Chem Engn, 350 Clyde Bldg, Provo, UT 84602 USA
关键词
waterflooding; well placement; history matching; recovery optimization; EOR; PARTICLE SWARM OPTIMIZATION; ENSEMBLE KALMAN FILTER; CRYOGENIC CARBON CAPTURE; JOINT OPTIMIZATION; DYNAMIC OPTIMIZATION; DIFFERENTIAL EVOLUTION; NUMERICAL-SIMULATION; FLOODING PROCESSES; PREDICTIVE CONTROL; GENETIC ALGORITHM;
D O I
10.3390/pr5030034
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
This paper presents a review of history matching and oil field development optimization techniques with a focus on optimization algorithms. History matching algorithms are reviewed as a precursor to production optimization algorithms. Techniques for history matching and production optimization are reviewed including global and local methods. Well placement, well control, and combined well placement-control optimization using both secondary and tertiary oil production techniques are considered. Secondary and tertiary recovery techniques are commonly referred to as waterflooding and enhanced oil recovery (EOR), respectively. Benchmark models for comparison of methods are summarized while other applications of methods are discussed throughout. No single optimization method is found to be universally superior. Key areas of future work are combining optimization methods and integrating multiple optimization processes. Current challenges and future research opportunities for improved model validation and large scale optimization algorithms are also discussed.
引用
收藏
页数:25
相关论文
共 35 条
  • [1] Infill well placement optimization for secondary development of waterflooding oilfields with SPSA algorithm
    Li, Congcong
    Fang, Chaoqiang
    Huang, Yougen
    Zuo, Hailong
    Zhang, Zhang
    Wang, Shuoliang
    FRONTIERS IN ENERGY RESEARCH, 2022, 10
  • [2] A review on optimization algorithms and surrogate models for reservoir automatic history matching
    Zhao, Yulong
    Luo, Ruike
    Li, Longxin
    Zhang, Ruihan
    Zhang, Deliang
    Zhang, Tao
    Xie, Zehao
    Luo, Shangui
    Zhang, Liehui
    GEOENERGY SCIENCE AND ENGINEERING, 2024, 233
  • [3] Well placement optimization subject to realistic field development constraints
    Jesmani, Mansoureh
    Bellout, Mathias C.
    Hanea, Remus
    Foss, Bjarne
    COMPUTATIONAL GEOSCIENCES, 2016, 20 (06) : 1185 - 1209
  • [4] A new approach in well placement optimization using metaheuristic algorithms
    Raji, Sajjad
    Dehnamaki, Arezoo
    Somee, Behzad
    Mahdiani, Mohammad Reza
    JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING, 2022, 215
  • [5] Production Well Placement and History Matching by Hyperparametric Optimization and Machine Learning
    Donskoi A.
    Medvedev A.
    Shchudro T.
    Terekhov K.
    Vassilevski Y.
    Lobachevskii Journal of Mathematics, 2024, 45 (1) : 166 - 176
  • [6] Method for Well Placement Optimization in Oil Field Development
    Andreeva, A. I.
    Afanasyev, A. A.
    MOSCOW UNIVERSITY MECHANICS BULLETIN, 2021, 76 (02) : 55 - 60
  • [7] Method for Well Placement Optimization in Oil Field Development
    A. I. Andreeva
    A. A. Afanasyev
    Moscow University Mechanics Bulletin, 2021, 76 : 55 - 60
  • [8] Well placement optimization subject to realistic field development constraints
    Mansoureh Jesmani
    Mathias C. Bellout
    Remus Hanea
    Bjarne Foss
    Computational Geosciences, 2016, 20 : 1185 - 1209
  • [9] A holistic review on artificial intelligence techniques for well placement optimization problem
    Islam, Jahedul
    Vasant, Pandian M.
    Negash, Berihun Mamo
    Laruccia, Moacyr Bartholomeu
    Myint, Myo
    Watada, Junzo
    ADVANCES IN ENGINEERING SOFTWARE, 2020, 141
  • [10] On optimization algorithms for the reservoir oil well placement problem
    Bangerth, W.
    Klie, H.
    Wheeler, M. F.
    Stoffa, P. L.
    Sen, M. K.
    COMPUTATIONAL GEOSCIENCES, 2006, 10 (03) : 303 - 319