GO2OGS 1.0: a versatile workflow to integrate complex geological information with fault data into numerical simulation models

Fischer, Thomas; Naumov, Dmitri; Sattler, Sabine; Kolditz, Olaf; Walther, Marc

doi:10.5194/gmd-8-3681-2015

Cited by 20 publications

(16 citation statements)

References 54 publications

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“…The stratigraphic model is based on well log data and geophysical data obtained from the TLUG. We used the workflow developed by Fischer et al (2015) to convert the data format, by which the complex 3-D geological model was converted into the open-source VTK format file that can be directly read by OGS.…”

Section: Aquifer Propertiesmentioning

confidence: 99%

Improved regional-scale groundwater representation by the coupling of the mesoscale Hydrologic Model (mHM v5.7) to the groundwater model OpenGeoSys (OGS)

et al. 2018

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Abstract. Most large-scale hydrologic models fall short in reproducing groundwater head dynamics and simulating transport process due to their oversimplified representation of groundwater flow. In this study, we aim to extend the applicability of the mesoscale Hydrologic Model (mHM v5.7) to subsurface hydrology by coupling it with the porous media simulator OpenGeoSys (OGS). The two models are one-way coupled through model interfaces GIS2FEM and RIV2FEM, by which the grid-based fluxes of groundwater recharge and the river–groundwater exchange generated by mHM are converted to fixed-flux boundary conditions of the groundwater model OGS. Specifically, the grid-based vertical reservoirs in mHM are completely preserved for the estimation of land-surface fluxes, while OGS acts as a plug-in to the original mHM modeling framework for groundwater flow and transport modeling. The applicability of the coupled model (mHM–OGS v1.0) is evaluated by a case study in the central European mesoscale river basin – Nägelstedt. Different time steps, i.e., daily in mHM and monthly in OGS, are used to account for fast surface flow and slow groundwater flow. Model calibration is conducted following a two-step procedure using discharge for mHM and long-term mean of groundwater head measurements for OGS. Based on the model summary statistics, namely the Nash–Sutcliffe model efficiency (NSE), the mean absolute error (MAE), and the interquartile range error (QRE), the coupled model is able to satisfactorily represent the dynamics of discharge and groundwater heads at several locations across the study basin. Our exemplary calculations show that the one-way coupled model can take advantage of the spatially explicit modeling capabilities of surface and groundwater hydrologic models and provide an adequate representation of the spatiotemporal behaviors of groundwater storage and heads, thus making it a valuable tool for addressing water resources and management problems.

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Section: Aquifer Propertiesmentioning

confidence: 99%

Improved regional-scale groundwater representation by the coupling of the mesoscale Hydrologic Model (mHM v5.7) to the groundwater model OpenGeoSys (OGS)

et al. 2018

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“…However, relying on a single model cannot reflect the inherent geological uncertainty (Neuman, 2003). Recent advances in geostatistics have shown the importance of using multiple model realizations for uncertainty quantification in many geoscience fields, including glaciology (e.g., Cullen et al, 2017), hydrogeology (e.g., Barfod et al, 2018;Zhou et al, 2014), hydrology (e.g., Goovaerts, 2000;Marko et al, 2014), hydrocarbon reservoir modeling (e.g., Caers and Zhang, 2004;Christie et al, 2002;Dutta et al, 2019;Yin et al, 2019), and geothermal (e.g., Rühaak et al, 2015;Vogt et al, 2010). Geostatistical approaches can provide multiple geological models that are conditioned or constrained to borehole data.…”

Section: Introductionmentioning

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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“…The preparation of process analyses involves integration of geoinformation data from heterogeneous sources, e.g., GIS maps and 3D hydrogeological data. The OGS DataExplorer software has been developed for this purpose and is also able to integrate simulation results from different modeling tools (Rink 2015;Fischer et al 2015). For the present study, integrated data and simulation results have been embedded into a Unity3D framework.…”

Section: D Spatial Data In Web-based and Visual Information Systemsmentioning

confidence: 99%

Energy storage in the geological subsurface: dimensioning, risk analysis and spatial planning: the ANGUS+ project

et al. 2016

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New techniques and methods for energy storage are required for the transition to a renewable power supply, termed ''Energiewende'' in Germany. Energy storage in the geological subsurface provides large potential capacities to bridge temporal gaps between periods of production of solar or wind power and consumer demand and may also help to relieve the power grids. Storage options include storage of synthetic methane, hydrogen or compressed air in salt caverns or porous formations as well as heat storage in porous formations. In the ANGUS? project, heat and gas storage in porous media and salt caverns and aspects of their use on subsurface spatial planning concepts are investigated. The optimal dimensioning of storage sites, the achievable charging and discharging rates and the effective storage capacity as well as the induced thermal, hydraulic, mechanical, geochemical and microbial effects are studied. The geological structures, the surface energy infrastructure and the governing processes are parameterized, using either literature data or own experimental studies. Numerical modeling tools are developed for the simulation of realistically defined synthetic storage scenarios. The feasible dimensioning of storage applications is assessed in sitespecific numerical scenario analyses, and the related spatial extents and time scales of induced effects connected with the respective storage application are quantified. Additionally, geophysical monitoring methods, which allow for a better spatial resolution of the storage operation, induced effects or leakages, are evaluated based on these scenario simulations. Methods for the assessment of such subsurface geological storage sites are thus developed, which account for the spatial extension of the subsurface operation itself as well as its induced effects and the spatial requirements of adequate monitoring methods.

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GO2OGS 1.0: a versatile workflow to integrate complex geological information with fault data into numerical simulation models

Cited by 20 publications

References 54 publications

Improved regional-scale groundwater representation by the coupling of the mesoscale Hydrologic Model (mHM v5.7) to the groundwater model OpenGeoSys (OGS)

Improved regional-scale groundwater representation by the coupling of the mesoscale Hydrologic Model (mHM v5.7) to the groundwater model OpenGeoSys (OGS)

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

Energy storage in the geological subsurface: dimensioning, risk analysis and spatial planning: the ANGUS+ project

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