Ensemble-based data assimilation in reservoir characterization: A review
This paper presents a review of ensemble-based data assimilation for strongly nonlinear
problems on the characterization of heterogeneous reservoirs with different production …
problems on the characterization of heterogeneous reservoirs with different production …
Machine learning assisted history matching for a deepwater lobe system
High exploration costs resulting in sparse datasets and complicated geological structures in
deepwater depositional systems make the reservoir characterization extremely difficult. To …
deepwater depositional systems make the reservoir characterization extremely difficult. To …
Efficient assessment of reservoir uncertainty using distance-based clustering: a review
This paper presents a review of 71 research papers related to a distance-based clustering
(DBC) technique for efficiently assessing reservoir uncertainty. The key to DBC is to select a …
(DBC) technique for efficiently assessing reservoir uncertainty. The key to DBC is to select a …
Geological model sampling using PCA-assisted support vector machine for reliable channel reservoir characterization
History matching is a crucial procedure for predicting reservoir performances and making
decisions. However, it is difficult due to uncertainties of initial reservoir models. Therefore, it …
decisions. However, it is difficult due to uncertainties of initial reservoir models. Therefore, it …
Machine-learning-based porosity estimation from multifrequency poststack seismic data
Estimating porosity models via seismic data is challenging due to the low signal-to-noise
ratio and insufficient resolution of the data. Although impedance inversion is often used in …
ratio and insufficient resolution of the data. Although impedance inversion is often used in …
Feature extraction using a deep learning algorithm for uncertainty quantification of channelized reservoirs
Reservoir models are generated by geostatistics using available static data. However, there
is inherent uncertainty in the reservoir models due to limited information. A number of …
is inherent uncertainty in the reservoir models due to limited information. A number of …
Conditioning generative adversarial networks on nonlinear data for subsurface flow model calibration and uncertainty quantification
SM Razak, B Jafarpour - Computational Geosciences, 2022 - Springer
Conditioning complex subsurface flow models on nonlinear data is complicated by the need
to preserve the expected geological connectivity patterns to maintain solution plausibility …
to preserve the expected geological connectivity patterns to maintain solution plausibility …
Model regeneration scheme using a deep learning algorithm for reliable uncertainty quantification of channel reservoirs
Reservoir characterization is one of the essential procedures for decision makings.
However, conventional inversion methods of history matching have several inevitable issues …
However, conventional inversion methods of history matching have several inevitable issues …
Identification of hydraulic conductivity field of a karst aquifer by using transition probability geostatistics and discrete cosine transform with an ensemble method
X Duan, Y Deng, X Chu, X Peng, H Su… - Hydrological …, 2022 - Wiley Online Library
Abstract Knowledge of the spatial distribution characteristics of hydraulic parameters is
essential for the management and protection of karst groundwater resources. In this study …
essential for the management and protection of karst groundwater resources. In this study …
Integration of an Iterative Update of Sparse Geologic Dictionaries with ES‐MDA for History Matching of Channelized Reservoirs
This study couples an iterative sparse coding in a transformed space with an ensemble
smoother with multiple data assimilation (ES‐MDA) for providing a set of geologically …
smoother with multiple data assimilation (ES‐MDA) for providing a set of geologically …