Data assimilation for a geological process model using the ensemble Kalman filter

Skauvold, Jacob; Eidsvik, Jo

doi:10.1111/bre.12273

Cited by 8 publications

(3 citation statements)

References 29 publications

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“…Statistical inversion of geological process models has been studied by e.g. Charvin et al (2009) who used Markov chain Monte Carlo sampling and Skauvold and Eidsvik (2018) who used variants of the EnKF and the ensemble smoother. The estimation task considered here is part of a larger data assimilation problem.…”

Section: Example: Geological Process Modelmentioning

confidence: 99%

Parametric spatial covariance models in the ensemble Kalman filter

Skauvold¹,

Eidsvik²

2019

Spatial Statistics

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Several applications rely on data assimilation methods for complex spatio-temporal problems. The focus of this paper is on ensemble-based methods, where some approaches require estimation of covariances between state variables and observations in the as-similation step. Spurious correlations present a challenge in such cases as they can degrade the quality of the ensemble represen-tation of probability distributions. In particular, prediction vari-ability is often underestimated. We propose to replace the sample covariance estimate by a parametric approach using maximum likelihood estimation for a small number of parameters in a spatial covariance model. Parametric covariance and precision estimation are employed in the context of the ensemble Kalman filter, and applied to a Gauss-linear autoregressive model and a geological process model. We learn that parametric approaches reduce the underestimation in prediction variability. Furthermore rich, non-stationary models do not seem to add much over simpler models with fewer parameters.

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Section: Example: Geological Process Modelmentioning

confidence: 99%

Parametric spatial covariance models in the ensemble Kalman filter

Skauvold¹,

Eidsvik²

2019

Spatial Statistics

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“…In this context a trial-and-error manual calibration is extremely time consuming and leads to a single (deterministic) solution. Probabilistic approaches have been proposed to address these issues 10 , 11 . Recently a few studies 8 , 12 presented automatic calibration procedures for the general purpose SFM Dionisos 13 .…”

Section: Introductionmentioning

confidence: 99%

Modelling parametric uncertainty in large-scale stratigraphic simulations

Mahmudova

Civa

Caronni

et al. 2023

Sci Rep

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We combine forward stratigraphic models with a suite of uncertainty quantification and stochastic model calibration algorithms for the characterization of sedimentary successions in large scale systems. The analysis focuses on the information value provided by a probabilistic approach in the modelling of large-scale sedimentary basins. Stratigraphic forward models (SFMs) require a large number of input parameters usually affected by uncertainty. Thus, model calibration requires considerable time both in terms of human and computational resources, an issue currently limiting the applications of SFMs. Our work tackles this issue through the combination of sensitivity analysis, model reduction techniques and machine learning-based optimization algorithms. We first employ a two-step parameter screening procedure to identify relevant parameters and their assumed probability distributions. After selecting a restricted set of important parameters these are calibrated against available information, i.e., the depth of interpreted stratigraphic surfaces. Because of the large costs associated with SFM simulations, probability distributions of model parameters and outputs are obtained through a data driven reduced complexity model. Our study demonstrates the numerical approaches by considering a portion of the Porcupine Basin, Ireland. Results of the analysis are postprocessed to assess (i) the uncertainty and practical identifiability of model parameters given a set of observations, (ii) spatial distribution of lithologies. We analyse here the occurrences of sand bodies pinching against the continental slope, these systems likely resulting from gravity driven processes in deep sea environment.

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“…In this setting a single (deterministic) calibration is not informative of the uncertainties associated with the system behavior and their implications in terms of geological scenarios. A stochastic model calibration strategy is required to evaluate the risk tied to the uncertainty of SFMs (Charvin et al 2009;Skauvold and Eidsvik 2018).…”

Section: Introductionmentioning

confidence: 99%

Stochastic Inverse Modeling and Parametric Uncertainty of Sediment Deposition Processes Across Geologic Time Scales

et al. 2020

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In this work an integrated methodological and operational framework for diagnosis and calibration of Stratigraphic Forward Models (SFMs) which are typically employed for the characterization of sedimentary basins is presented. Model diagnosis rests on local and global sensitivity analysis tools and leads to quantification of the relative importance of uncertain model parameters on modeling goals of interest. Model calibration is performed in a stochastic framework, leading to estimates of distributions of model parameters (and ensuing spatial distributions of model outputs) conditional on available information. Starting from a considerable number of uncertain model parameters, which is typically associated with SFMs of the kind analyzed, the approach leads to the identification of a reduced set of parameters which are most influential to drive stratigraphic modeling results. Probability distributions of these model parameters conditional on available data are then evaluated through stochastic inverse modeling. To alleviate computational efforts, this step is performed through a combination of a surrogate model constructed through the Polynomial Chaos Expansion approach and a machine learning algorithm for efficient search of the parameter space during model inversion. As a test bed for the workflow, focus is on a realistic synthetic three-dimensional scenario which is modeled through a widely used SFM that enables one to perform three-dimensional numerical simulations of the accumulation of siliciclastic and carbonate sediments across geologic time scales. These results constitute a robust basis upon which further deployment of the approach to industrial field settings can be designed.

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Data assimilation for a geological process model using the ensemble Kalman filter

Cited by 8 publications

References 29 publications

Parametric spatial covariance models in the ensemble Kalman filter

Parametric spatial covariance models in the ensemble Kalman filter

Modelling parametric uncertainty in large-scale stratigraphic simulations

Stochastic Inverse Modeling and Parametric Uncertainty of Sediment Deposition Processes Across Geologic Time Scales

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