Recent progress on reservoir history matching: a review
History matching is a type of inverse problem in which observed reservoir behavior is used
to estimate reservoir model variables that caused the behavior. Obtaining even a single …
to estimate reservoir model variables that caused the behavior. Obtaining even a single …
Inversion using a new low-dimensional representation of complex binary geological media based on a deep neural network
Efficient and high-fidelity prior sampling and inversion for complex geological media is still a
largely unsolved challenge. Here, we use a deep neural network of the variational …
largely unsolved challenge. Here, we use a deep neural network of the variational …
Closed-loop reservoir management
JD Jansen, SD Douma, DR Brouwer… - SPE Reservoir …, 2009 - onepetro.org
Closed-loop reservoir management is a combination of model-based optimization and data
assimilation (computer-assisted history matching), also referred to as 'real-time reservoir …
assimilation (computer-assisted history matching), also referred to as 'real-time reservoir …
Process control in conventional oil and gas fields—Challenges and opportunities
B Foss - Control Engineering Practice, 2012 - Elsevier
This paper presents some key operational challenges in the upstream petroleum industries
in which advanced control and model-based optimization has or may have a significant …
in which advanced control and model-based optimization has or may have a significant …
Efficient real-time reservoir management using adjoint-based optimal control and model updating
The key ingredients to successful real-time reservoir management, also known as a “closed-
loop” approach, include efficient optimization and model-updating (history-matching) …
loop” approach, include efficient optimization and model-updating (history-matching) …
A deep-learning-based geological parameterization for history matching complex models
A new low-dimensional parameterization based on principal component analysis (PCA) and
convolutional neural networks (CNN) is developed to represent complex geological models …
convolutional neural networks (CNN) is developed to represent complex geological models …
History matching of three-phase flow production data
Adjoint equations for 3D, three-phase flow problems are developed and implemented to
calculate the sensitivity of production data to permeability fields and well skin factors …
calculate the sensitivity of production data to permeability fields and well skin factors …
Kernel principal component analysis for efficient, differentiable parameterization of multipoint geostatistics
This paper describes a novel approach for creating an efficient, general, and differentiable
parameterization of large-scale non-Gaussian, non-stationary random fields (represented by …
parameterization of large-scale non-Gaussian, non-stationary random fields (represented by …
A new differentiable parameterization based on principal component analysis for the low-dimensional representation of complex geological models
A new approach based on principal component analysis (PCA) for the representation of
complex geological models in terms of a small number of parameters is presented. The …
complex geological models in terms of a small number of parameters is presented. The …
History matching with an ensemble Kalman filter and discrete cosine parameterization
History matching of large hydrocarbon reservoirs is challenging because of several reasons
including:(1) scarcity of available measurements relative to the number of unknowns …
including:(1) scarcity of available measurements relative to the number of unknowns …