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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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Porosity prediction using ensemble machine learning approaches: A case study from Upper Assam basin
Porosity is an important petrophysical parameter that determines the amount of fluid, including oil, water, and gas contained within the rock. In...
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A robust adaptive iterative ensemble smoother scheme for practical history matching applications
Much of the recent work on history matching reservoir models has focused on the iterative Ensemble Smoother (iES) method. This is well suited for...
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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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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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EnRML for History Matching Petroleum Models
In this chapter, we present an application of an iterative ensemble smoother for a history-matching case with a reservoir simulator. The application...
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Optimal reduction of anthropogenic emissions for air pollution control and the retrieval of emission source from observed pollutants III: Emission source inversion using a double correction iterative method
Using the incomplete adjoint operator method in part I of this series of papers, the total emission source S can be retrieved from the pollutant...
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Groundwater contamination source estimation based on a refined particle filter associated with a deep residual neural network surrogate
Groundwater contamination source estimation (GCSE) involves an inverse process to match time-series monitoring data in sparse observation wells. It...
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Accounting for model errors in iterative ensemble smoothers
In the strong-constraint formulation of the history-matching problem, we assume that all the model errors relate to a selection of uncertain model...
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3Dvar and SC-4DVar for the Lorenz 63 Model
In this chapter, we study the workings of 3DVar and SC-4DVar on the same chaotic Lorenz 1963 system as used with ensemble methods in Chap....
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On spatially correlated observations in importance sampling methods for subsidence estimation
The particle filter is a data assimilation method based on importance sampling for state and parameter estimation. We apply a particle filter in two...
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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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Deep learning-aided image-oriented history matching of geophysical data
Various types of geophysical measurements have been made available to illuminate different characteristics of subsurface reservoir formations. It...
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Comparison of regularized ensemble Kalman filter and tempered ensemble transform particle filter for an elliptic inverse problem with uncertain boundary conditions
In this paper, we focus on parameter estimation for an elliptic inverse problem. We consider a 2D steady-state single-phase Darcy flow model, where...
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Geostatistical Rock Physics Inversion for Predicting the Spatial Distribution of Porosity and Saturation in the Critical Zone
Understanding the subsurface structure and function in the near-surface groundwater system, including fluid flow, geomechanical, and weathering...
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Pore Pressure Uncertainty Characterization Coupling Machine Learning and Geostatistical Modelling
Pore pressure prediction is fundamental when drilling deep and geologically complex reservoirs. Even in relatively well-characterized hydrocarbon...
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A quasi-Newton trust-region method for optimization under uncertainty using stochastic simplex approximate gradients
The goal of field-development optimization is maximizing the expected value of an objective function, e.g., net present value for a producing oil...
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Localization and Inflation
Localization and inflation have become essential means of mitigating the effects of the low-rank approximation in ensemble methods. Localization...
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Ground motions induced by pore pressure changes at the Szentes geothermal area, SE Hungary
Excessive thermal water volumes have been extracted from porous sedimentary rocks in the Hungarian part of the Pannonian Basin. Thermal water...
