Dynamic data integration for structural modeling: model screening approach using a distance-based model parameterization

@article{suzuki:hal-04066553,
 abstract = {This paper proposes a novel history-matching method where reservoir structure is inverted from dynamic fluid flow response. The proposed workflow consists of searching for models that match production history from a large set of prior structural model realizations. This prior set represents the reservoir structural uncertainty because of interpretation uncertainty on seismic sections. To make such a search effective, we introduce a parameter space defined with a “similarity distance” for accommodating this large set of realizations. The inverse solutions are found using a stochastic search method. Realistic reservoir examples are presented to prove the applicability of the proposed method.},
 author = {Suzuki, Satomi and Caumon, Guillaume and Caers, Jef},
 doi = {10.1007/s10596-007-9063-9},
 hal_id = {hal-04066553},
 hal_version = {v1},
 journal = {{Computational Geosciences}},
 keywords = {History matching ; Data assimilation ; Structural uncertainty ; Discrete-space optimization ; Distance-based model parameterization ; Distance function ; Stochastic search},
 month = {March},
 number = {1},
 pages = {105-119},
 publisher = {{Springer Verlag}},
 title = {{Dynamic data integration for structural modeling: model screening approach using a distance-based model parameterization}},
 url = {https://hal.univ-lorraine.fr/hal-04066553},
 volume = {12},
 year = {2008}
}