Recent progress on reservoir history matching: a review
By Dean S. Oliver, Yan Chen. 2011
736 citations recorded by the source corpus.
Subfields: Environmental Engineering, Mechanical Engineering, Ocean Engineering
View Paper: https://www.semanticscholar.org/paper/13d02fc8ce9f2e887c28add0b9625d8efbc78a68
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Environmental Engineering
- Louis J. Durlofsky
- Liangping Li
- Haiyan Zhou
- Philippe Renard
- Wolfgang Nowak
- J. Jaime Gómez-Hernández
- Tapan Mukerji
- Niklas Linde
In Mechanical Engineering
- Louis J. Durlofsky
- Jan Dirk Jansen
- Kody J. H. Law
- Andrew M. Stuart
- Wenyue Sun
- Yan Chen
- Ahmed H. Elsheikh
- Hamidreza Hamdi
In Ocean Engineering
- Louis J. Durlofsky
- Jan Dirk Jansen
- Wenyue Sun
- Andrew M. Stuart
- Kody J. H. Law
- Yan Chen
- Ahmed H. Elsheikh
- Dean S. Oliver
Citations in the corpus
- Connectivity metrics for subsurface flow and transport (2013, background): “There is a very wide range of techniques that have been developed to solve the inverse problem [142,143].”
- Inverse methods in hydrogeology: Evolution and recent trends (2014, methodology): “In103 the present paper we do not intend to review all current inverse methods, since several others have reviewed104 the topic from different points of view (e.g., Carrera et al., 2005; De Marsily et al., 2000; McLaughlin and105 Townley, 1996; Oliver and Chen, 2010; Yeh, 1986).”
- Ensemble Randomized Maximum Likelihood Method as an Iterative Ensemble Smoother (2011, methodology): “The EnKF does not require an adjoint system to obtain the gradient and has been demonstrated to be quite flexible in the types of variable that can be estimated and the types of measurement that can be used as data (see reviews in Aanonsen et al. (2009), Oliver and Chen (2011)).”
- Geological realism in hydrogeological and geophysical inverse modeling: A review (2015, background): “The ensemble Kalman filter (EnKf; [1,45,72,131]) is an approach to data-based forecasting that has recently gained attention for subsurface inverse problems.”
- Gaussian Processes for history-matching: application to an unconventional gas reservoir (2017, background): “data assimilation methods can be defined in a minimization context [6], they are generally classified as uncertainty quantification techniques [7, 8].”
- Theoretical connections between optimization algorithms based on an approximate gradient (2013, neutral): “6 of [25].”
- 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): “The state of the art and its recent progress for history matching are presented in detail in the review paper [63].”
- A New Data-Space Inversion Procedure for Efficient Uncertainty Quantification in Subsurface Flow Problems (2017, background): “Data assimilation for history-matching oil/gas reservoirs is described in the book by Oliver et al. (2008) and, more recently, in the review paper by Oliver and Chen (2011).”
- Model-reduced gradient-based history matching (2011, neutral): “Oliver et al. (2008)).”
- A New Differentiable Parameterization Based on Principal Component Analysis for the Low-Dimensional Representation of Complex Geological Models (2014, background): “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.”
- Bayesian Reservoir History Matching Considering Model and Parameter Uncertainties (2012, methodology): “These optimization problems can be solved using gradient based methods like the Broyden–Fletcher–Goldfarb–Shanno (BFGS) method (Zhang and Reynolds 2002; Nocedal and Wright 2006) and its limited memory variants.”
- Evaluation of Gaussian approximations for data assimilation in reservoir models (2012, methodology): “It is worth mentioning that issues of the EnKF and EnSRF due to sampling error have been often reported [6, 27] and used as motivation for covariance regularization.”
- Use of reduced-order models in well control optimization (2017, positive): “The use of optimization for different aspects of reservoir characterization and management is discussed in, e.g., Echeverrı́a Ciaurri et al. (2011b), Jansen (2011), Oliver and Chen (2011) and Velez-Langs (2005).”
- Surrogate accelerated sampling of reservoir models with complex structures using sparse polynomial chaos expansion (2015, background): “A review of recent research activities on subsurface flow model calibration can be found in [9, 66].”