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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/gmd-2019-232
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2019-232
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: development and technical paper 22 Oct 2019

Submitted as: development and technical paper | 22 Oct 2019

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This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).

Automated Monte Carlo-based Quantification and Updating of Geological Uncertainty with Borehole Data (AutoBEL v1.0)

Zhen Yin1, Sebastien Strebelle2, and Jef Caers1 Zhen Yin et al.
  • 1Department of Geological Sciences, Stanford University, Stanford, CA 94305, USA
  • 2Chevron, Houston, USA

Abstract. We provide an automated method for uncertainty quantification and updating of geological models using borehole data for subsurface developments (groundwater, geothermal, oil & gas, and CO2 sequestration, etc.) within a Bayesian framework. Our methodologies are developed with the Bayesian Evidential Learning protocol for uncertainty quantification. Under such framework, newly acquired borehole data directly and jointly update geological models (structure, lithology, petrophysics and fluids), globally and spatially, without time-consuming model re-buildings. To address the above, 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 falsification process, a sequential direct forecasting is designed to perform the joint uncertainty quantification. The direct forecasting is a data-scientific 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, allows for fast computation posterior geological models. The proposed framework is completely automated in an opensource project. We demonstrate its application by applying to a generalized synthetic dataset motivated by a gas reservoir from Australia. 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.

Zhen Yin et al.
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Status: open (until 18 Dec 2019)
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