A cross-validation framework to extract data features for reducing structural uncertainty in subsurface heterogeneity

Lopez-Alvis, Jorge; Hermans, Thomas; Nguyen, Frédéric

doi:10.1016/j.advwatres.2019.103427

Cited by 9 publications

(11 citation statements)

References 33 publications

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“…A prior consistency check before training may indicate if the VAE fails due to the first reason. In this work, this check was done using a methodology based on a low‐dimensional representation of the data (Hermans et al., 2015; Lopez‐Alvis et al., 2019; Park et al., 2013; Scheidt et al., 2018) according to which none of the TIs is falsified, that is, all the proposed patterns are likely to have generated the data. The details are shown in Supporting Information S1.…”

Section: Resultsmentioning

confidence: 99%

Geophysical Inversion Using a Variational Autoencoder to Model an Assembled Spatial Prior Uncertainty

et al. 2022

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Prior information regarding subsurface spatial patterns may be used in geophysical inversion to obtain realistic subsurface models. Field experiments require prior information with sufficiently diverse patterns to accurately estimate the spatial distribution of geophysical properties in the sensed subsurface domain. A variational autoencoder (VAE) provides a way to assemble all patterns deemed possible in a single prior distribution. Such patterns may include those defined by different base training images and also their perturbed versions, for example, those resulting from geologically consistent operations such as erosion/dilation, local deformation, and intrafacies variability. Once the VAE is trained, inversion may be done in the latent space which ensures that inverted models have the patterns defined by the assembled prior. Gradient‐based inversion with both a synthetic and a field case of cross‐borehole GPR traveltime data shows that using the VAE assembled prior performs as good as using the VAE trained on the pattern with the best fit, but it has the advantage of lower computation cost and more realistic prior uncertainty. Moreover, the synthetic case shows an adequate estimation of most small‐scale structures. The absolute values of wave velocity are computed by assuming a linear mixing model which involves two additional parameters that effectively shift and scale velocity values and are included in the inversion.

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Section: Resultsmentioning

confidence: 99%

Geophysical Inversion Using a Variational Autoencoder to Model an Assembled Spatial Prior Uncertainty

et al. 2022

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“…This approach can be applied to examine the effectiveness of monitoring and the monitoring duration to lower uncertainty in risk metrics, such as top-layer CO 2 saturation and plume mobility and seismic time-lapse data. Accordingly, it will also be useful to apply the DF procedures to more complex geological models, such as bimodal channelized systems, which can be challenging for traditional (model-based) history matching methods, kernel density estimation [45], and extensions of CCA [46] can be included in the BEL framework to tackle more complex nonlinear inverse problems. Finally, using data space inversion (DSI), as described by Sun and Durlofsky [26], CO 2 leakage detection under uncertainty should also be considered.…”

Section: Discussion and Concluding Remarksmentioning

confidence: 99%

Managing Uncertainty in Geological CO2 Storage Using Bayesian Evidential Learning

Tadjer

Bratvold

2021

Energies

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Carbon capture and storage (CCS) has been increasingly looking like a promising strategy to reduce CO2 emissions and meet the Paris agreement’s climate target. To ensure that CCS is safe and successful, an efficient monitoring program that will prevent storage reservoir leakage and drinking water contamination in groundwater aquifers must be implemented. However, geologic CO2 sequestration (GCS) sites are not completely certain about the geological properties, which makes it difficult to predict the behavior of the injected gases, CO2 brine leakage rates through wellbores, and CO2 plume migration. Significant effort is required to observe how CO2 behaves in reservoirs. A key question is: Will the CO2 injection and storage behave as expected, and can we anticipate leakages? History matching of reservoir models can mitigate uncertainty towards a predictive strategy. It could prove challenging to develop a set of history matching models that preserve geological realism. A new Bayesian evidential learning (BEL) protocol for uncertainty quantification was released through literature, as an alternative to the model-space inversion in the history-matching approach. Consequently, an ensemble of previous geological models was developed using a prior distribution’s Monte Carlo simulation, followed by direct forecasting (DF) for joint uncertainty quantification. The goal of this work is to use prior models to identify a statistical relationship between data prediction, ensemble models, and data variables, without any explicit model inversion. The paper also introduces a new DF implementation using an ensemble smoother and shows that the new implementation can make the computation more robust than the standard method. The Utsira saline aquifer west of Norway is used to exemplify BEL’s ability to predict the CO2 mass and leakages and improve decision support regarding CO2 storage projects.

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“…This has been done by treating the interpretation independent of other model variables in some studies (e.g., Aydin and Caers, 2017;Grose et al, 2018;Wellmann et al, 2010). For example, one could first update the probabilities of geological scenarios, then update the other variables (Lopez-Alvis et al, 2019). Regarding the automation of BEL, its intermediate steps can also be adjusted depending on users' specific applications.…”

Section: Discussionmentioning

confidence: 99%

Automated Monte Carlo-based quantification and updating of geological uncertainty with borehole data (AutoBEL v1.0)

2020

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Abstract. Geological uncertainty quantification is critical to subsurface modeling and prediction, such as groundwater, oil or gas, and geothermal resources, and needs to be continuously updated with new data. We provide an automated method for uncertainty quantification and the updating of geological models using borehole data for subsurface developments within a Bayesian framework. Our methodologies are developed with the Bayesian evidential learning protocol for uncertainty quantification. Under such a framework, newly acquired borehole data directly and jointly update geological models (structure, lithology, petrophysics, and fluids), globally and spatially, without time-consuming model rebuilding. To address the above matters, an ensemble of prior geological models is first constructed by Monte Carlo simulation from prior distribution. Once the prior model is tested by means of a falsification process, a sequential direct forecasting is designed to perform the joint uncertainty quantification. The direct forecasting is a statistical learning method that learns from a series of bijective operations to establish “Bayes–linear-Gauss” statistical relationships between model and data variables. Such statistical relationships, once conditioned to actual borehole measurements, allow for fast-computation posterior geological models. The proposed framework is completely automated in an open-source project. We demonstrate its application by applying it to a generic gas reservoir dataset. The posterior results show significant uncertainty reduction in both spatial geological model and gas volume prediction and cannot be falsified by new borehole observations. Furthermore, our automated framework completes the entire uncertainty quantification process efficiently for such large models.

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A cross-validation framework to extract data features for reducing structural uncertainty in subsurface heterogeneity

Cited by 9 publications

References 33 publications

Geophysical Inversion Using a Variational Autoencoder to Model an Assembled Spatial Prior Uncertainty

Geophysical Inversion Using a Variational Autoencoder to Model an Assembled Spatial Prior Uncertainty

Managing Uncertainty in Geological CO2 Storage Using Bayesian Evidential Learning

Automated Monte Carlo-based quantification and updating of geological uncertainty with borehole data (AutoBEL v1.0)

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