Reservoir history matching represents a crucial stage in the reservoir development process
and purposes to match model predictions with various observed field data, including …

This paper proposes a novel approach based on deep learning to improve oil reservoirs'
history matching problem. Deep autoencoders are widely used to solve the oil industry …

In present days, Iterative ensemble smoothers (IES) are among the main methods to perform
ensemble-based history matching in petroleum reservoirs. Generally, some localization …

Deep learning-based surrogate models have been a promising way of dealing with the
computational effort of data assimilation. Although the surrogate can reduce the …

Time-lapse-seismic-data assimilation has been drawing the reservoir-engineering
community's attention over the past few years. One of the advantages of including this kind …

In this work, we study the ensemble size influence on an adaptive ensemble-based
methodology for history matching of petroleum reservoirs. The assimilation scheme used is …