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Experimental sandbox tracer tests to characterize a two-facies aquifer via an ensemble smoother
Estimating aquifer properties and their spatial variability is the most challenging part of groundwater flow and transport simulations. In this work,...
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Ensemble Smoother with Multiple Data Assimilation as a Tool for Curve Fitting and Parameter Uncertainty Characterization: Example Applications to Fit Nonlinear Sorption Isotherms
The ensemble smoother with multiple data assimilation (ES-MDA) coupled to a normal-score transformation is used to fit a Langmuir isotherm curve to...
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Well-testing based turbidite lobes modeling using the ensemble smoother with multiple data assimilation
The representation of geological bodies is a difficult task, which involves a large number of parameters and assumptions that are commonly simplified...
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Variational Autoencoder or Generative Adversarial Networks? A Comparison of Two Deep Learning Methods for Flow and Transport Data Assimilation
Groundwater modeling is an important tool for water resources management and aquifer remediation. However, the inherent strong heterogeneity of the...
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Sequential multilevel assimilation of inverted seismic data
We consider estimation of absolute permeability from inverted seismic data. Large amounts of simultaneous data, such as inverted seismic data,...
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Ensemble-Based Seismic and Production Data Assimilation Using Selection Kalman Model
Data assimilation in reservoir modeling often involves model variables that are multimodal, such as porosity and permeability. Well established data...
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Ensemble-based history matching of the Edvard Grieg field using 4D seismic data
The Edvard Grieg field is a highly complex and heterogeneous reservoir with an extensive fault structure and a mixture of sandstone, conglomerate,...
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Flexible iterative ensemble smoother for calibration of perfect and imperfect models
Iterative ensemble smoothers have been widely used for calibrating simulators of various physical systems due to the relatively low computational...
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Recent developments combining ensemble smoother and deep generative networks for facies history matching
Ensemble smoothers are among the most successful and efficient techniques currently available for history matching. However, because these methods...
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Using a machine learning proxy for localization in ensemble data assimilation
Ensemble data assimilation methods, particularly iterative forms of ensemble smoother, are very useful assisted history matching techniques. One of...
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Constrained iterative ensemble smoother for multi solution search assisted history matching
History matching algorithms usually converge to the most prominent solution in the hypercube of parameter space defined by bound values. Here, we...
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Randomized Tensor Decomposition for Large-Scale Data Assimilation Problems for Carbon Dioxide Sequestration
Data assimilation methods are commonly used to predict petrophysical properties of deep saline aquifers for carbon dioxide sequestration studies....
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Batch seismic inversion using the iterative ensemble Kalman smoother
An ensemble-based method for seismic inversion to estimate elastic attributes is considered, namely the iterative ensemble Kalman smoother. The main...
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Data-space inversion with ensemble smoother
Reservoir engineers use large-scale numerical models to predict the production performance in oil and gas fields. However, these models are...
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Low-Rank Ensemble Methods
This chapter willError covariance matrix introduce another approximation where we represent all state error covariances using a finite ensemble of...
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Ensemble-Based Electrical Resistivity Tomography with Data and Model Space Compression
Inversion of electrical resistivity tomography (ERT) data is an ill-posed problem that is usually solved through deterministic gradient-based...
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Bridging Deep Convolutional Autoencoders and Ensemble Smoothers for Improved Estimation of Channelized Reservoirs
One of the main problems associated with applying data assimilation methods for facies models is the lack of geological plausibility in updates. This...
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A novel methodological approach for land subsidence prediction through data assimilation techniques
Anthropogenic land subsidence can be evaluated and predicted by numerical models, which are often built over deterministic analyses. However,...
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Identification of non-Gaussian parameters in heterogeneous aquifers by a modified probability conditioning method through hydraulic-head assimilation
Parameter estimation with uncertainty quantification is essential in groundwater modeling to ensure model quality; however, parameter estimation,...
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An integrated framework for optimal monitoring and history matching in CO\(_{2}\) storage projects
Monitoring is an important component of geological carbon storage operations because it provides data that can be used to estimate key quantities...