Data-Driven Model Falsification and Uncertainty Quantification for Fractured Reservoirs

Fang, Junling; Gong, Bin; Caers, Jef

doi:10.1016/j.eng.2022.04.015

Cited by 9 publications

(2 citation statements)

References 59 publications

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“…More advanced methodologies for model diagnostics can be found, for example, in [3,10,13,14,21,24,26,28]. Support vector machine and tree-based regression are utilized, for example, in [10] and described also in [26, Chapter 2]. The methodologies in [14,21,24] utilize a type of Bayesian model averaging to compute model probabilities.…”

Section: Introductionmentioning

confidence: 99%

Multiscale model diagnostics

Mannseth

2024

Comput Geosci

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I consider the problem of model diagnostics, that is, the problem of criticizing a model prior to history matching by comparing data to an ensemble of simulated data based on the prior model (prior predictions). If the data are not deemed as a credible prior prediction by the model diagnostics, some settings of the model should be changed before history matching is attempted. I particularly target methodologies that are computationally feasible for large models with large amounts of data. A multiscale methodology, that can be applied to analyze differences between data and prior predictions in a scale-by-scale fashion, is proposed for this purpose. The methodology is computationally inexpensive, straightforward to apply, and can handle correlated observation errors without making approximations. The multiscale methodology is tested on a set of toy models, on two simplistic reservoir models with synthetic data, and on real data and prior predictions from the Norne field. The tests include comparisons with a previously published method (termed the Mahalanobis methodology in this paper). For the Norne case, both methodologies led to the same decisions regarding whether to accept or discard the data as a credible prior prediction. The multiscale methodology led to correct decisions for the toy models and the simplistic reservoir models. For these models, the Mahalanobis methodology either led to incorrect decisions, and/or was unstable with respect to selection of the ensemble of prior predictions.

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Section: Introductionmentioning

confidence: 99%

Multiscale model diagnostics

Mannseth

2024

Comput Geosci

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“…Alternatives have been developed to estimate the posterior distribution at a limited cost such as Kalman ensemble generators [16,17] or Bayesian Evidential learning (BEL) [18,19]. BEL is a simulation-based prediction approach that has been initially proposed to by-pass the difficult calibration of subsurface reservoir models and to directly forecast targets from the data [20,21], with recent applications in geothermal energy [22][23][24], reservoir modelling [25][26][27][28], experimental design [29] and geotechnics [30]. It has also been quickly adopted by geophysicists to integrate geophysical data into model or properties prediction [24,31,32].…”

Section: Introductionmentioning

confidence: 99%

Assessing and Improving the Robustness of Bayesian Evidential Learning in One Dimension for Inverting Time-Domain Electromagnetic Data: Introducing a New Threshold Procedure

Ahmed,

Aigner,

Michel

et al. 2024

Water

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Understanding the subsurface is of prime importance for many geological and hydrogeological applications. Geophysical methods offer an economical alternative for investigating the subsurface compared to costly borehole investigations. However, geophysical results are commonly obtained through deterministic inversion of data whose solution is non-unique. Alternatively, stochastic inversions investigate the full uncertainty range of the obtained models, yet are computationally more expensive. In this research, we investigate the robustness of the recently introduced Bayesian evidential learning in one dimension (BEL1D) for the stochastic inversion of time-domain electromagnetic data (TDEM). First, we analyse the impact of the accuracy of the numerical forward solver on the posterior distribution, and derive a compromise between accuracy and computational time. We also introduce a threshold-rejection method based on the data misfit after the first iteration, circumventing the need for further BEL1D iterations. Moreover, we analyse the impact of the prior-model space on the results. We apply the new BEL1D with a threshold approach on field data collected in the Luy River catchment (Vietnam) to delineate saltwater intrusions. Our results show that the proper selection of time and space discretization is essential for limiting the computational cost while maintaining the accuracy of the posterior estimation. The selection of the prior distribution has a direct impact on fitting the observed data and is crucial for a realistic uncertainty quantification. The application of BEL1D for stochastic TDEM inversion is an efficient approach, as it allows us to estimate the uncertainty at a limited cost.

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