History Matching Geostatistical Model Realizations Using a Geometrical Domain Based Parameterization Technique

被引:5
作者
Ding, Didier Yu [1 ]
Roggero, Frederic [1 ]
机构
[1] Inst Francais Petrole, Reservoir Engn Dept, F-92852 Rueil Malmaison, France
关键词
Constrained geostatistical realization; Gaussian white noise; Gradual deformation; Local parameterization; History matching; CALIBRATION; SIMULATIONS;
D O I
10.1007/s11004-010-9273-x
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
Reservoir characterization needs the integration of various data through history matching, especially dynamic information such as production or 4D seismic data. Although reservoir heterogeneities are commonly generated using geostatistical models, random realizations cannot generally match observed dynamic data. To constrain model realizations to reproduce measured dynamic data, an optimization procedure may be applied in an attempt to minimize an objective function, which quantifies the mismatch between real and simulated data. Such assisted history matching methods require a parameterization of the geostatistical model to allow the updating of an initial model realization. However, there are only a few parameterization methods available to update geostatistical models in a way consistent with the underlying geostatistical properties. This paper presents a local domain parameterization technique that updates geostatistical realizations using assisted history matching. This technique allows us to locally change model realizations through the variation of geometrical domains whose geometry and size can be easily controlled and parameterized. This approach provides a new way to parameterize geostatistical realizations in order to improve history matching efficiency.
引用
收藏
页码:413 / 432
页数:20
相关论文
共 50 条
[31]   Accelerated Bayesian inference-based history matching of petroleum reservoirs using polynomial chaos expansions [J].
Khatoon, Sufia ;
Phirani, Jyoti ;
Bahga, Supreet Singh .
INVERSE PROBLEMS IN SCIENCE AND ENGINEERING, 2021, 29 (13) :3086-3116
[32]   History matching for exploring and reducing climate model parameter space using observations and a large perturbed physics ensemble [J].
Williamson, Daniel ;
Goldstein, Michael ;
Allison, Lesley ;
Blaker, Adam ;
Challenor, Peter ;
Jackson, Laura ;
Yamazaki, Kuniko .
CLIMATE DYNAMICS, 2013, 41 (7-8) :1703-1729
[33]   History matching for exploring and reducing climate model parameter space using observations and a large perturbed physics ensemble [J].
Daniel Williamson ;
Michael Goldstein ;
Lesley Allison ;
Adam Blaker ;
Peter Challenor ;
Laura Jackson ;
Kuniko Yamazaki .
Climate Dynamics, 2013, 41 :1703-1729
[34]   An efficient transformer-based surrogate model with end-to-end training strategies for automatic history matching [J].
Zhang, Jinding ;
Kang, Jinzheng ;
Zhang, Kai ;
Zhang, Liming ;
Liu, Piyang ;
Liu, Xingyu ;
Sun, Weijia ;
Wang, Guangyao .
GEOENERGY SCIENCE AND ENGINEERING, 2024, 240
[35]   History matching and production optimization of water flooding based on a data-driven interwell numerical simulation model [J].
Zhao, Hui ;
Li, Ying ;
Cui, Shuyue ;
Shang, Genhua ;
Reynolds, Albert C. ;
Guo, Zhenyu ;
Li, Huazhou Andy .
JOURNAL OF NATURAL GAS SCIENCE AND ENGINEERING, 2016, 31 :48-66
[36]   Reservoir automatic history matching method using ensemble Kalman filter based on shrinkage covariance matrix estimation [J].
Jing, Cao .
GEOSYSTEM ENGINEERING, 2023, 26 (02) :39-47
[37]   Uncertainty Quantification in Reservoir Prediction: Part 1Model Realism in History Matching Using Geological Prior Definitions [J].
Arnold, Dan ;
Demyanov, Vasily ;
Rojas, Temistocles ;
Christie, Mike .
MATHEMATICAL GEOSCIENCES, 2019, 51 (02) :209-240
[38]   A vector-to-sequence based multilayer recurrent network surrogate model for history matching of large-scale reservoir [J].
Ma, Xiaopeng ;
Zhang, Kai ;
Zhao, Hanjun ;
Zhang, Liming ;
Wang, Jian ;
Zhang, Huaqing ;
Liu, Piyang ;
Yan, Xia ;
Yang, Yongfei .
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING, 2022, 214
[39]   Assisted history matching for the inversion of fractures based on discrete fracture-matrix model with different combinations of inversion parameters [J].
Kai Zhang ;
Xiaoming Zhang ;
Liming Zhang ;
Lixin Li ;
Hai Sun ;
Zhaoqin Huang ;
Jun Yao .
Computational Geosciences, 2017, 21 :1365-1383
[40]   Gaussian Processes Proxy Model with Latent Variable Models and Variogram-Based Sensitivity Analysis for Assisted History Matching [J].
Zhang, Dongmei ;
Zhang, Yuyang ;
Jiang, Bohou ;
Jiang, Xinwei ;
Kang, Zhijiang .
ENERGIES, 2020, 13 (17)