Articles | Volume 24, issue 10
https://doi.org/10.5194/hess-24-4997-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-24-4997-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
3D multiple-point statistics simulations of the Roussillon Continental Pliocene aquifer using DeeSse
Valentin Dall'Alba
CORRESPONDING AUTHOR
Center of Hydrogeology and Geothermics (CHYN), University of Neuchâtel, Rue Emile-Argand 11, 2000 Neuchâtel, Switzerland
Philippe Renard
Center of Hydrogeology and Geothermics (CHYN), University of Neuchâtel, Rue Emile-Argand 11, 2000 Neuchâtel, Switzerland
Julien Straubhaar
Center of Hydrogeology and Geothermics (CHYN), University of Neuchâtel, Rue Emile-Argand 11, 2000 Neuchâtel, Switzerland
Benoit Issautier
BRGM, Georessource Division, Sedimentary Basin Team, Orléans, France
Cédric Duvail
Fugro France SAS, Castries, France
Yvan Caballero
BRGM, Univ. Montpellier, Montpellier, France
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Cited articles
Aunay, B., Duvail, C., Giordana, G., Doerfliger, N., Le Strat, P., Montginoul, M., and Pistre, S.: A pluridisciplinary methodology for integrated management of a coastal aquifer: Geological, hydrogeological and
economic studies of the Roussillon aquifer (Pyrénées-Orientales, France), Observatoire Océanologique – Laboratoire Arago, Vie et Milieu/Life & Environment, 56, 275–285, 2006. a, b
Barfod, A. A. S., Møller, I., Christiansen, A. V., Høyer, A.-S., Hoffimann, J., Straubhaar, J., and Caers, J.: Hydrostratigraphic modeling using multiple-point statistics and airborne transient electromagnetic methods, Hydrol. Earth Syst. Sci., 22, 3351–3373, https://doi.org/10.5194/hess-22-3351-2018, 2018. a
Boisvert, J. B., Pyrcz, M. J., and Deutsch, C. V.: Multiple-point statistics for training image selection, Nat. Resourc. Res., 16, 313–321, https://doi.org/10.1007/s11053-008-9058-9, 2007. a
Caballero, Y. and Ladouche, B.: Impact of climate change on groundwater in a confined Mediterranean aquifer, Hydrol. Earth Syst. Sci. Discuss., 12, 10109–10156, https://doi.org/10.5194/hessd-12-10109-2015, 2015. a
Chauveau, M., Chazot, S., Perrin, C., Bourgin, P.-Y., Sauquet, E., Vidal, J.-P., Rouchy, N., Martin, E., David, J., Norotte, T., Maugis, P., and De Lacaze, X.: Quels impacts des changements climatiques sur les eaux de
surface en France à l'horizon 2070?, Houille Blanche, 4, 5–15, https://doi.org/10.1051/lhb/2013027, 2013. a
Chugunova, T. and Hu, L.: Multiple-Point simulations constrained by continuous auxiliary data, Math. Geosci., 40, 133–146, https://doi.org/10.1007/s11004-007-9142-4, 2008. a, b
Clauzon, G., Le Strat, P., Duvail, C., Do Couto, D., Suc, J. P., Molliex, S., Bache, F., Besson, D., Lindsay, E. H., Opdyke, N. D., Rubino, J. L., Popescu, S. M., Haq, B. U., and Gorini, C.: The Roussillon Basin (S. France): A case-study to distinguish local and regional events between 6 and 3 Ma, Mar. Petrol. Geol., 66, 18–40, https://doi.org/10.1016/j.marpetgeo.2015.03.012, 2015. a
Colombera, L., Felletti, F., Mountney, N. P., and McCaffrey, W. D.: A database approach for constraining stochastic simulations of the sedimentary heterogeneity of fluvial reservoirs, AAPG Bull., 96, 2143–2166, https://doi.org/10.1306/04211211179, 2012. a
Comunian, A., Renard, P., and Straubhaar, J.: 3D multiple-point statistics simulation using 2D training images, Comput. Geosci., 40, 49–65, https://doi.org/10.1016/j.cageo.2011.07.009, 2012. a
Cordua, K. S., Hansen, T. M., Gulbrandsen, M. L., Barnes, C., and Mosegaard, K.: Mixed-point geostatistical simulation: A combination of two- and multiple-point geostatistics, Geophys. Res. Lett., 43, 9030–9037, 2016. a
Dall'Alba, V.: Roussillon MPS 2020 dataset Dall'Alba [Data set], Zenodo., https://doi.org/10.5281/zenodo.4110278, 2020. a
de Carvalho, P. R. M., da Costa, J. F. C. L., Rasera, L. G., and Varella, L. E. S.: Geostatistical facies simulation with geometric patterns of a petroleum reservoir, Stoch. Environ. Res. Risk A., 31, 1805–1822, https://doi.org/10.1007/s00477-016-1243-5, 2017. a, b
de Marsily, G., Delay, F., Gonçalvès, J., Renard, P., Teles, V., and Violette, S.: Dealing with spatial heterogeneity, Hydrogeol. J., 13, 161–183, https://doi.org/10.1007/s10040-004-0432-3, 2005. a
Duvail, C.: Expression des facteurs régionaux et locaux dans l'enregistrement sédimentaire d'une marge passive. Exemple de la marge du Golfe du Lion, étudiée selon un continuum terre-mer, Université
de Montpellier, Montpellier, 262 pp., 2008. a
Duvail, C., Gorini, C., Lofi, J., Le Strat, P., Clauzon, G., and dos Reis, A. T.: Correlation between onshore and offshore Pliocene-Quaternary systems tracts below the Roussillon Basin (eastern Pyrenees, France), Mar. Petrol. Geol., 22, 747–756, https://doi.org/10.1016/j.marpetgeo.2005.03.009, 2005. a
Genna, A.: Carte géologique harmonisée du département des Pyrénées-Orientales, Notice technique, Rapport final RP-57032FR, Tech. rep., BRGM, Orléans, France, 2009. a
Hoffimann, J., Scheidt, C., Barfod, A., and Caers, J.: Stochastic simulation by image quilting of process-based geological models, Comput. Geosci., 106, 18–32, https://doi.org/10.1016/j.cageo.2017.05.012, 2017. a
Høyer, A.-S., Vignoli, G., Hansen, T. M., Vu, L. T., Keefer, D. A., and Jørgensen, F.: Multiple-point statistical simulation for hydrogeological models: 3-D training image development and conditioning strategies, Hydrol. Earth Syst. Sci., 21, 6069–6089, https://doi.org/10.5194/hess-21-6069-2017, 2017. a, b
Hu, L. Y. and Chugunova, T.: Multiple-point geostatistics for modeling subsurface heterogeneity: A comprehensive review, Water Resour. Res., 44, 1–14, https://doi.org/10.1029/2008WR006993, 2008. a
Journel, A. G.: Geostatistics for Conditional Simulation of Ore Bodies, Econ. Geol., 69, 673–687, https://doi.org/10.2113/gsecongeo.69.5.673, 1974. a
Juda, P., Renard, P., and Straubhaar, J.: A framework for the cross-validation of categorical geostatistical simulations, Earth Space Sci., 7, e2020EA001152, https://doi.org/10.1029/2020ea001152, 2020. a
Koltermann, C. E. and Gorelick, S. M.: Heterogeneity in sedimentary deposits: A review of structure-imitating, process-imitating, and descriptive approaches, Water Resour. Res., 32, 2617–2658, 1996. a
Lofi, J., Gorini, C., Berné, S., Clauzon, G., Dos Reis, A. T., Ryan, W. B., and Steckler, M. S.: Erosional processes and paleo-environmental changes in the Western Gulf of Lions (SW France) during the Messinian Salinity Crisis, Mar. Geol., 217, 1–30, https://doi.org/10.1016/j.margeo.2005.02.014, 2005. a
Mariethoz, G. and Caers, J.: Multiple-Point Geostatistics: stochastic modeling with training images, Wiley-Blackwell, John Wiley and Sons, LTD, New York, USA, 2014. a
Mariethoz, G., Renard, P., and Straubhaar, J.: The direct sampling method to perform multiple-point geostatistical simulations, Water Resour. Res., 46, 1–14, https://doi.org/10.1029/2008WR007621, 2010. a, b, c, d
Matheron, G.: Principles of geostatistics, Econ. Geol., 58, 1246–1266, https://doi.org/10.2113/gsecongeo.58.8.1246, 1963. a, b
Matheron, G., Beucher, H., de Fouquet, C., Galli, A., Guerillot, D., and Ravenne, C.: Conditional simulation of the geometry of fluvio-deltaic reservoirs, Society of Petroleum Engineers, in: PE Annual Technical Conference and Exhibition, 27–30 September 1987, Dallas, Texas, 1987. a
Meerschman, E., Pirot, G., Mariethoz, G., Straubhaar, J., Van Meirvenne, M., and Renard, P.: A practical guide to performing multiple-point statistical simulations with the Direct Sampling algorithm, Comput. Geosci., 52, 307–324, https://doi.org/10.1016/j.cageo.2012.09.019, 2013. a
Mood, A. M.: The distribution theory of runs, Ann. Math. Stat., 11, 367–392, 1940. a
Naranjo-Fernández, N., Guardiola-Albert, C., and Montero-González, E.: Applying 3D geostatistical simulation to improve the groundwater management modelling of sedimentary aquifers: The case of Doñana (Southwest Spain), Water, 11, 39, https://doi.org/10.3390/w11010039, 2018. a
Nichols, G. J. and Fisher, J. A.: Processes, facies and architecture of fluvial distributary system deposits, Sediment. Geol., 195, 75–90, https://doi.org/10.1016/j.sedgeo.2006.07.004, 2007. a
Serra, O. and Sulpice, L.: Sedimentological analysis of shale-sand series from well logs, Society of Petrophysicists and Well-Log Analysts, in: SPWLA 16th Annual Logging Symposium, 4–7 June 1975, New Orleans, Louisiana, 1975. a
Shannon, C. E.: A Mathematical Theory of Communication, Bell Syst. Tech. J., 27, 379–423, https://doi.org/10.1002/j.1538-7305.1948.tb01338.x, 1948. a
Straubhaar, J.: DeeSse user's guide, Tech. rep., The Centre for Hydrogeology and Geothermics (CHYN), University of Neuchâtel, Neuchâtel, Switzerland, 2019. a
Straubhaar, J., Renard, P., Mariethoz, G., Froidevaux, R., and Besson, O.: An improved parallel multiple-point algorithm using a list approach, Math. Geosci., 43, 305–328, https://doi.org/10.1007/s11004-011-9328-7, 2011. a
Straubhaar, J., Walgenwitz, A., and Renard, P.: Parallel Multiple-Point Statistics Algorithm Based on List and Tree Structures, Math. Geosci., 45, 131–147, https://doi.org/10.1007/s11004-012-9437-y, 2013. a
Straubhaar, J., Renard, P., and Mariethoz, G.: Conditioning multiple-point statistics simulations to block data, Spat. Stat., 16, 53–71, https://doi.org/10.1016/j.spasta.2016.02.005, 2016. a
Straubhaar, J., Renard, P., and Chugunova, T.: Multiple-point statistics using multi-resolution images, Stoch. Environ. Res. Risk A., 34, 251–273, https://doi.org/10.1007/s00477-020-01770-8, 2020. a
Strebelle, S., Payrazyan, K., and Caers, J.: Modeling of a Deepwater Turbidite Reservoir Conditional to Seismic Data Using Multiple-Point Geostatistics, in: SPE Annual Technical Conference and Exhibition, in: SPE Annual Technical Conference and Exhibition, 29 September–2 October 2002, San Antonio, Texas, https://doi.org/10.2118/77425-MS, 2002. a, b, c
Tahmasebi, P., Hezarkhani, A., and Sahimi, M.: Multiple-point geostatistical modeling based on the cross-correlation functions, Computat. Geosci., 16, 779–797, https://doi.org/10.1007/s10596-012-9287-1, 2012. a
Wellmann, J. F. and Regenauer-lieb, K.: Uncertainties have a meaning: Information entropy as a quality measure for 3-D geological models, Tectonophysics, 526–529, 207–216, https://doi.org/10.1016/j.tecto.2011.05.001, 2012. a
Zhang, T., Switzer, P., and Journel, A.: Filter-based classification of training image patterns for spatial simulation, Math. Geol., 38, 63–80, https://doi.org/10.1007/s11004-005-9004-x, 2006. a
Short summary
Due to climate and population evolution, increased pressure is put on the groundwater resource, which calls for better understanding and models. In this paper, we describe a novel workflow to model the geological heterogeneity of coastal aquifers and apply it to the Roussillon plain (southern France). The main strength of the workflow is its capability to model aquifer heterogeneity when only sparse data are available while honoring the local geological trends and quantifying uncertainty.
Due to climate and population evolution, increased pressure is put on the groundwater resource,...