A Visual Analytics Framework for Exploring Uncertainties in Reservoir Models

Zahra Sahaf, Hamidreza Hamdi, Roberta Cabral Ramos Mota, Mario Costa Sousa, Frank Maurer

Abstract

Geological uncertainty is an essential element that affects the prediction of hydrocarbon production. The standard approach to address the geological uncertainty is to generate a large number of random 3D geological models and then perform flow simulations for each of them. Such a brute-force approach is not efficient as the flow simulations are computationally costly and as a result, domain experts cannot afford running a large number of simulations. Therefore, it is critically important to be able to address the uncertainty using a few geological models, which can reasonably represent the overall uncertainty of the ensemble. Our goal is to design and develop a visual analytics framework to filter the geological models and to only select models that can potentially cover the uncertain space. This framework is based on the mutual information for the calculation of the distance between the models and clustering for the grouping of similar models. Interactive visualization tasks have also been designed to make the whole process more understandable. Finally, we evaluated our results by comparing with the existent brute force approach.

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Paper Citation


in Harvard Style

Sahaf Z., Hamdi H., Cabral Ramos Mota R., Costa Sousa M. and Maurer F. (2018). A Visual Analytics Framework for Exploring Uncertainties in Reservoir Models.In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP, ISBN 978-989-758-289-9, pages 74-84. DOI: 10.5220/0006608500740084


in Bibtex Style

@conference{ivapp18,
author={Zahra Sahaf and Hamidreza Hamdi and Roberta Cabral Ramos Mota and Mario Costa Sousa and Frank Maurer},
title={A Visual Analytics Framework for Exploring Uncertainties in Reservoir Models},
booktitle={Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP,},
year={2018},
pages={74-84},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006608500740084},
isbn={978-989-758-289-9},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP,
TI - A Visual Analytics Framework for Exploring Uncertainties in Reservoir Models
SN - 978-989-758-289-9
AU - Sahaf Z.
AU - Hamdi H.
AU - Cabral Ramos Mota R.
AU - Costa Sousa M.
AU - Maurer F.
PY - 2018
SP - 74
EP - 84
DO - 10.5220/0006608500740084