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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/gmd-2020-136
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2020-136
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: development and technical paper 18 Aug 2020

Submitted as: development and technical paper | 18 Aug 2020

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This preprint is currently under review for the journal GMD.

Constraining stochastic 3-D structural geological models with topology information using Approximate Bayesian Computation using GemPy 2.1

Alexander Schaaf1,2, Miguel de la Varga2, Florian Wellmann2, and Clare E. Bond1 Alexander Schaaf et al.
  • 1Geology and Petroleum Geology, School of Geosciences, University of Aberdeen, AB24 3UE, UK
  • 2Computational Geoscience and Reservoir Engineering, RWTH Aachen University, Aachen, Germany

Abstract. Structural geomodeling is a key technology for the visualization and quantification of subsurface systems. Given the limited data and the resulting necessity for geological interpretation to construct these geomodels, uncertainty is pervasive and traditionally unquantified. Probabilistic geomodeling allows for the simulation of uncertainties by automatically constructing geomodels from perturbed input data sampled from probability distributions. But random sampling of input parameters can lead to construction of geomodels that are unrealistic, either due to modeling artefacts or by not matching known information about the regional geology of the modeled system. We present here a method to incorporate geological information in the form of geomodel topology into stochastic simulations to constrain resulting probabilistic geomodel ensembles. Simulated geomodel realisations are checked against topology information using a likelihood-free Approximate Bayesian Computation approach. We demonstrate how we can learn our input data parameter (prior) distributions on topology information in two experiments: (1) A synthetic geomodel using a rejection sampling scheme (ABC-REJ) to demonstrate the approach; (2) A geomodel of a subset of the Gullfaks field in the North Sea, comparing both rejection sampling and a Sequential Monte Carlo sampler (ABC-SMC). We also discuss possible speed-ups of using more advanced sampling techniques to avoid simulation of unfeasible geomodels in the first place. Results demonstrate the feasibility to use topology as a summary statistic, to restrict the generation of model ensembles with additional geological information and to obtain improved ensembles of probable geomodels using stochastic simulation methods.

Alexander Schaaf et al.

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Alexander Schaaf et al.

Alexander Schaaf et al.

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Latest update: 19 Oct 2020
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Short summary
Uncertainty is an inherent property of any model of the subsurface. We show how geological topology information – how different regions of rocks in the subsurface are connected – can be used to learn uncertain geological models to reduce uncertainty. More widely, the method demonstrates the use of probabilistic machine learning (Bayesian inference) to learn structural geological models on auxiliary geological knowledge that can be encoded in graph structures.
Uncertainty is an inherent property of any model of the subsurface. We show how geological...
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