A new methodology to train fracture network simulation using multiple-point statistics
<p>Natural fracture network characteristics can be establishes from high-resolution outcrop images acquired from drone and photogrammetry. Such images might also be good analogues of subsurface naturally fractured reservoirs and can be used to make predictions of the fracture geometry and e...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2019-04-01
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| Series: | Solid Earth |
| Online Access: | https://www.solid-earth.net/10/537/2019/se-10-537-2019.pdf |
| _version_ | 1819268174795767808 |
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| author | P.-O. Bruna J. Straubhaar R. Prabhakaran R. Prabhakaran G. Bertotti K. Bisdom G. Mariethoz M. Meda |
| author_facet | P.-O. Bruna J. Straubhaar R. Prabhakaran R. Prabhakaran G. Bertotti K. Bisdom G. Mariethoz M. Meda |
| author_sort | P.-O. Bruna |
| collection | DOAJ |
| description | <p>Natural fracture network characteristics can be establishes from high-resolution
outcrop images acquired from drone and photogrammetry. Such images might
also be good analogues of subsurface naturally fractured reservoirs and can
be used to make predictions of the fracture geometry and efficiency at
depth. However, even when supplementing fractured reservoir models with
outcrop data, gaps will remain in the model and fracture network
extrapolation methods are required. In this paper we used fracture networks
interpreted from two outcrops from the Apodi area, Brazil, to present a
revised and innovative method of fracture network geometry prediction using
the multiple-point statistics (MPS) method.</p><p>The MPS method presented in this article uses a series of small synthetic
training images (TIs) representing the geological variability of fracture
parameters observed locally in the field. The TIs contain the statistical
characteristics of the network (i.e. orientation, spacing, length/height and
topology) and allow for the representation of a complex arrangement of fracture networks.
These images are flexible, as they can be simply sketched by the user.</p><p>We proposed to simultaneously use a set of training images in specific
elementary zones of the Apodi outcrops in order to best replicate the
non-stationarity of the reference network. A sensitivity analysis was
conducted to emphasise the influence of the conditioning data, the
simulation parameters and the training images used. Fracture density
computations were performed on selected realisations and compared to the
reference outcrop fracture interpretation to qualitatively evaluate the
accuracy of our simulations. The method proposed here is adaptable in terms
of training images and probability maps to ensure that the geological complexity
in the simulation process is accounted for. It can be used on any type of
rock containing natural fractures in any kind of tectonic context. This
workflow can also be applied to the subsurface to predict the fracture
arrangement and fluid flow efficiency in water, geothermal or hydrocarbon
fractured reservoirs.</p> |
| first_indexed | 2024-12-23T21:28:52Z |
| format | Article |
| id | doaj.art-aa987db04a8a4e50ab1f4d25e11e3bcb |
| institution | Directory Open Access Journal |
| issn | 1869-9510 1869-9529 |
| language | English |
| last_indexed | 2024-12-23T21:28:52Z |
| publishDate | 2019-04-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Solid Earth |
| spelling | doaj.art-aa987db04a8a4e50ab1f4d25e11e3bcb2022-12-21T17:30:30ZengCopernicus PublicationsSolid Earth1869-95101869-95292019-04-011053755910.5194/se-10-537-2019A new methodology to train fracture network simulation using multiple-point statisticsP.-O. Bruna0J. Straubhaar1R. Prabhakaran2R. Prabhakaran3G. Bertotti4K. Bisdom5G. Mariethoz6M. Meda7Department of Geoscience and Engineering, Delft University of Technology, Delft, the NetherlandsCentre d'hydrogéologie et de géothermie (CHYN), Université de Neuchâtel, Emile-Argand 11, 2000 Neuchâtel, SwitzerlandDepartment of Geoscience and Engineering, Delft University of Technology, Delft, the NetherlandsDepartment of Mechanical Engineering, Section of Energy Technology, Eindhoven University of Technology, Eindhoven, the NetherlandsDepartment of Geoscience and Engineering, Delft University of Technology, Delft, the NetherlandsShell Global Solutions International B.V., Grasweg 31, 1031HW Amsterdam, the NetherlandsUniversity of Lausanne, Institute of Earth Surface Dynamics (IDYST) UNIL-Mouline, Geopolis, office 3337, 1015 Lausanne, SwitzerlandENI Spa, Upstream and Technical Services, San Donato Milanese, Italy<p>Natural fracture network characteristics can be establishes from high-resolution outcrop images acquired from drone and photogrammetry. Such images might also be good analogues of subsurface naturally fractured reservoirs and can be used to make predictions of the fracture geometry and efficiency at depth. However, even when supplementing fractured reservoir models with outcrop data, gaps will remain in the model and fracture network extrapolation methods are required. In this paper we used fracture networks interpreted from two outcrops from the Apodi area, Brazil, to present a revised and innovative method of fracture network geometry prediction using the multiple-point statistics (MPS) method.</p><p>The MPS method presented in this article uses a series of small synthetic training images (TIs) representing the geological variability of fracture parameters observed locally in the field. The TIs contain the statistical characteristics of the network (i.e. orientation, spacing, length/height and topology) and allow for the representation of a complex arrangement of fracture networks. These images are flexible, as they can be simply sketched by the user.</p><p>We proposed to simultaneously use a set of training images in specific elementary zones of the Apodi outcrops in order to best replicate the non-stationarity of the reference network. A sensitivity analysis was conducted to emphasise the influence of the conditioning data, the simulation parameters and the training images used. Fracture density computations were performed on selected realisations and compared to the reference outcrop fracture interpretation to qualitatively evaluate the accuracy of our simulations. The method proposed here is adaptable in terms of training images and probability maps to ensure that the geological complexity in the simulation process is accounted for. It can be used on any type of rock containing natural fractures in any kind of tectonic context. This workflow can also be applied to the subsurface to predict the fracture arrangement and fluid flow efficiency in water, geothermal or hydrocarbon fractured reservoirs.</p>https://www.solid-earth.net/10/537/2019/se-10-537-2019.pdf |
| spellingShingle | P.-O. Bruna J. Straubhaar R. Prabhakaran R. Prabhakaran G. Bertotti K. Bisdom G. Mariethoz M. Meda A new methodology to train fracture network simulation using multiple-point statistics Solid Earth |
| title | A new methodology to train fracture network simulation using multiple-point statistics |
| title_full | A new methodology to train fracture network simulation using multiple-point statistics |
| title_fullStr | A new methodology to train fracture network simulation using multiple-point statistics |
| title_full_unstemmed | A new methodology to train fracture network simulation using multiple-point statistics |
| title_short | A new methodology to train fracture network simulation using multiple-point statistics |
| title_sort | new methodology to train fracture network simulation using multiple point statistics |
| url | https://www.solid-earth.net/10/537/2019/se-10-537-2019.pdf |
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