Inverse Theory for Petroleum Reservoir Characterization and History Matching

By Dean S. Oliver, Albert C. Reynolds, Ning Liu. 2008

737 citations recorded by the source corpus.

Subfields: Mechanical Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/67323245bf494741ffea92326e47a19ecaa8f98a

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Citations in the corpus

A New Data-Space Inversion Procedure for Efficient Uncertainty Quantification in Subsurface Flow Problems (2017, methodology): “When the model variablesm are Gaussian, and the observations dobs are related to model variables through dobs = g(m)+ , themodel-space RMLmethod, as described by Oliver et al. (2008), proceeds as follows: 1.”
A New Differentiable Parameterization Based on Principal Component Analysis for the Low-Dimensional Representation of Complex Geological Models (2014, methodology): “Here, we will only highlight a few of these studies; see Oliver et al. (2008) for a comprehensive discussion of history matching and Oliver and Chen (2011) for a recent review.”
Uncertainty quantification of reservoir performance predictions using a stochastic optimization algorithm (2011, background): “The large-scale reservoir history matching problems considered in the Bayesian framework involve minimization of an objective function, which represents the mismatch of the predicted data and the observed data and the mismatch of the current reservoir model and the prior reservoir model [21, 28, 33]”
Analysis of regression confidence intervals and Bayesian credible intervals for uncertainty quantification (2012, methodology): “The Maximum a Posteriori (MAP) method [Carrera and Neuman, 1986; Oliver et al., 2008; Liu et al., 2010] is essentially equivalent to the regression-with-prior method used in this work, but was developed from the Bayesian perspective.”
Evaluation of Gaussian approximations for data assimilation in reservoir models (2012, neutral): “Under a Gaussian prior and a linear model, it can be shown that all these techniques provide samples of the posterior distribution [20, 16].”
Use of reduced-order models in well control optimization (2017, methodology): “Among such ‘quasi-Newton’ methods the limited-memory Broyden-Fletcher-Goldfarb-Shannon (LBFGS) method is particularly suited for very large problems; see Oliver et al. (2008).”
Theoretical connections between optimization algorithms based on an approximate gradient (2013, background): “Although, gradient-based optimization algorithms for maximizing or minimizing an objective or cost function do converge to a local optimum, it is apparent that when the number of controls is large, the value of the objective function at such a local optimum is often close to the global optimum becau”
Seismic inversion for reservoir properties combining statistical rock physics and geostatistics: A review (2010, background): “Redistribution subject to ich discipline, and many excellent books on geophysical inverse heory exist e.g., Menke, 1984; Tarantola, 1987; Parker, 1994; Sen nd Stoffa, 1995; Oliver et al., 2008 .”
Carbon Dioxide Storage Capacity of Organic-Rich Shales (2011, methodology): “Details of the automated nonlinear history-matching process are described elsewhere (Oliver et al. 2008).”
Bayesian updating via bootstrap filtering combined with data-driven polynomial chaos expansions: methodology and application to history matching for carbon dioxide storage in geological formations (2013, background): “Another important point about history matching techniques is that they can produce nonunique solutions [62], which means that several virtual models and parameter sets can match the observation data equally well.”
Data Assimilation: A Mathematical Introduction (2015, methodology): “randomized maximum likelihood method described in [93], and widely used in petroleum applications; the idea is discussed in detail in the context of the EnKF in [67].”
Surrogate accelerated sampling of reservoir models with complex structures using sparse polynomial chaos expansion (2015, methodology): “However, typical uses of MCMC methods need more than 10(5) steps to sample from the target distribution with a reasonable error [24].”