Imperial College London
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DrPabloSalinas

Faculty of Engineering, Department of Earth Science & Engineering

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pablo.salinas

 
 
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Location

 

Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Titus:2021:10.1007/s10596-021-10088-5,
author = {Titus, Z and Heaney, C and Jacquemyn, C and Salinas, P and Jackson, MD and Pain, C},
doi = {10.1007/s10596-021-10088-5},
journal = {Computational Geosciences: modeling, simulation and data analysis},
pages = {779--802},
title = {Conditioning surface-based geological models to well data using artificial neural networks},
url = {http://dx.doi.org/10.1007/s10596-021-10088-5},
volume = {26},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Surface-based modelling provides a computationally efficient approach for generating geometrically realistic representations of heterogeneity in reservoir models. However, conditioning Surface-Based Geological Models (SBGMs) to well data can be challenging because it is an ill-posed inverse problem with spatially distributed parameters. To aid fast and efficient conditioning, we use here SBGMs that model geometries using parametric, grid-free surfaces that require few parameters to represent even realistic geological architectures. A neural network is trained to learn the underlying process of generating SBGMs by learning the relationship between the parametrized SBGM inputs and the resulting facies identified at well locations. To condition the SBGM to these observed data, inverse modelling of the SBGM inputs is achieved by replacing the forward model with the pre-trained neural network and optimizing the network inputs using the back-propagation technique applied in training the neural network. An analysis of the uncertainties associated with the conditioned realisations demonstrates the applicability of the approach for evaluating spatial variations in geological heterogeneity away from control data in reservoir modelling. This approach for generating geologically plausible models that are calibrated with observed well data could also be extended to other geological modelling techniques such as object- and process-based modelling.
AU  - Titus,Z
AU  - Heaney,C
AU  - Jacquemyn,C
AU  - Salinas,P
AU  - Jackson,MD
AU  - Pain,C
DO  - 10.1007/s10596-021-10088-5
EP  - 802
PY  - 2021///
SN  - 1420-0597
SP  - 779
TI  - Conditioning surface-based geological models to well data using artificial neural networks
T2  - Computational Geosciences: modeling, simulation and data analysis
UR  - http://dx.doi.org/10.1007/s10596-021-10088-5
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000696749600001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://link.springer.com/article/10.1007%2Fs10596-021-10088-5
UR  - http://hdl.handle.net/10044/1/92355
VL  - 26
ER  -
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