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Automatic Determination of Sedimentary Units from Well Data

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Abstract

The issue of identifying stratigraphic units within a sedimentary succession is of prime importance for reservoir studies, because it allows splitting the reservoir into several units with specific parameters, thus reducing the vertical nonstationarity in simulations. A new method is proposed for semi-automatic determination of the sedimentary units from well logging that uses a customized geostatistical hierarchical clustering algorithm. A new linkage criteria derived from the Ward criteria (cluster minimum variance) is proposed to enforce the monotonic increase of dissimilarities. The discretized proportion of sand lithofacies calculated from the vertical proportion curve of the well is taken as input data. At each step of the procedure, the algorithm merges the most similar of two consecutive units of sand lithofacies, ensuring stratigraphic consistency. Finally, the number of units is deduced from the first most important step of the dissimilarity. The user can investigate a larger number of units by considering the clusters with lower levels of dissimilarities. The method is validated using two synthetic cases built for a fluvial meandering reservoir analog containing three and five units. The results from the synthetic cases show that the units are identified when the sand proportion contrast between units is larger than the internal variability within the units. For low sand contrasts between units or for a small number of wells, sedimentary unit limits may be found for lower clustering dissimilarities. Finally, the method is successfully applied to a field study, where the resulting cluster units are found to be comparable to the field interpretation, suggesting a limit between units defined by paleosols rather than close overlying lacustrine levels.

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References

  • Allard D, Guillot G (2000) Clustering geostatistical data. In: Proceedings of the sixth geostatistical conference

  • Allen DB, Pranter MJ (2016) Geologically constrained electrofacies classification of fluvial deposits: an example from the Cretaceous Mesaverde Group, Uinta and Piceance Basins. AAPG Bull 100:1775–1801. https://doi.org/10.1306/05131614229

    Article  Google Scholar 

  • Armstrong M, Galli A, Beucher H, Loc’h G, Renard D, Doligez B, Eschard R, Geffroy F (2011) Plurigaussian simulations in geosciences. Springer, Berlin

    Book  Google Scholar 

  • Berkhin P (2006) A survey of clustering data mining techniques. Grouping multidimensional data. Springer, Berlin, pp 25–71

    Google Scholar 

  • Catuneanu O (2002) Sequence stratigraphy of clastic systems: concepts, merits, and pitfalls. J Afr Earth Sci 35:1–43. https://doi.org/10.1016/S0899-5362(02)00004-0

    Article  Google Scholar 

  • Catuneanu O (2006) Principles of sequence stratigraphy. Elsevier, Boston

    Google Scholar 

  • Cojan I, Geffroy F, Laratte S, Rigollet C (2006) Process-based and stochastic modeling of fluvial meandering system. From model to field case study: example of the Loranca Miocene succession (Spain). In: Presented at the 17th international sedimentological congress, Fukuoka, Japan, August

  • Cojan I, Rivoirard J, Renard D (2009) From outcrop to process-based reservoir modelling of fluvial meandering systems. The key issue of parameter choice. In: Presented at the from river to rock record, January 12

  • Daams R, Díaz-Molina M, Mas R (1996) Uncertainties in the stratigraphic analysis of fluvial deposits from the Loranca Basin, central Spain. Sediment Geol 102:187–209. https://doi.org/10.1016/0037-0738(95)00062-3

    Article  Google Scholar 

  • Diaz-Molina M, Bustillo A, Capote R, Lopez-Martinez N (1985) Wet fluvial fans of the Loranca Basin (Central Spain), channel models and distal bioturbated gypsum with chert. 37

  • Edwards J, Lallier F, Caumon G, Carpentier C (2017) Uncertainty management in stratigraphic well correlation and stratigraphic architectures: a training-based method. Comput Geosci 111:1. https://doi.org/10.1016/j.cageo.2017.10.008

    Article  Google Scholar 

  • Fang JH, Chen HC, Shultz AW, Mahmoud W (1992) Computer-aided well log correlation. AAPG Bull 76:307

    Google Scholar 

  • Ferraretti D, Gamberoni G, Lamma E (2012) I2AM: a semi-automatic system for data interpretation in petroleum geology. In: PAI, pp 14–20

  • Fouedjio F (2016) A hierarchical clustering method for multivariate geostatistical data. Spat Stat 18:333–351. https://doi.org/10.1016/j.spasta.2016.07.003

    Article  Google Scholar 

  • Hammer Ø, Harper DAT (2006) Paleontological data analysis. Blackwell, Oxford

    Google Scholar 

  • Hill E, Robertson J, Uvarova Y (2015) Multiscale hierarchical domaining and compression of drill hole data. Comput Geosci. https://doi.org/10.1016/j.cageo.2015.03.005

    Article  Google Scholar 

  • Lance GN, Williams WT (1967) A general theory of classificatory sorting strategies: 1. Hierarchical systems. Comput J 9:373–380

    Article  Google Scholar 

  • Lapkovsky VV, Istomin AV, Kontorovich VA, Berdov VA (2015) Correlation of well logs as a multidimensional optimization problem. Russ Geol Geophys 56:487–492. https://doi.org/10.1016/j.rgg.2015.02.009

    Article  Google Scholar 

  • Lopez S (2003) Modélisation de réservoirs chenalisés méandriformes : une approche génétique et stochastique. https://pastel.archives-ouvertes.fr/pastel-00000630/document

  • Lopez S, Cojan I, Rivoirard J, Galli A (2008) Process-based stochastic modelling: meandering channelized reservoirs. In: Analogue and numerical modelling of sedimentary systems: from understanding to prediction. Wiley-Blackwell, New York, pp 139–144

    Chapter  Google Scholar 

  • Luthi SM, Bryant ID (1997) Well-log correlation using a back-propagation neural network. Math Geol 29:413–425

    Article  Google Scholar 

  • Martinius AW (2000) Labyrinthine facies architecture of the Tortola Fluvial System and controls on deposition (Late Oligocene-Early Miocene, Loranca Basin, Spain). J Sediment Res 70:850–867. https://doi.org/10.1306/2DC4093D-0E47-11D7-8643000102C1865D

    Article  Google Scholar 

  • Milligan GW (1979) Ultrametric hierarchical clustering algorithms. Psychometrika 44:343–346. https://doi.org/10.1007/BF02294699

    Article  Google Scholar 

  • MINES ParisTech, ARMINES (2016) Flumy Software v4.104. http://cg.ensmp.fr/flumy

  • Mirowski P, Herron M, Fluckiger S, Seleznev N, McCormick D (2005) New software for well-to-well correlation of spectroscopy logs. In: Presented at the AAPG international conference, Paris, France

  • Parks JM (1966) Cluster analysis applied to multivariate geologic problems. J Geol 74:703–715. https://doi.org/10.1086/627205

    Article  Google Scholar 

  • Ratcliffe KT, Wright AM, Hallsworth C, Morton A, Zaitlin BA, Potocki D, Wray DS (2004) An example of alternative correlation techniques in a low-accommodation setting, nonmarine hydrocarbon system: the (Lower Cretaceous) mannville basal quartz succession of southern Alberta. AAPG Bull 88:1419–1432. https://doi.org/10.1306/05100402035

    Article  Google Scholar 

  • Ravenne C, Galli A, Doligez B, Beucher H, Eschard R (2002) Quantification of facies relationships via proportion curves. Geostatistics Rio 2000. Springer, Dordrecht, pp 19–39

    Google Scholar 

  • Romary T, Ors F, Rivoirard J, Deraisme J (2015) Unsupervised classification of multivariate geostatistical data: two algorithms. Comput Geosci 85:96–103

    Article  Google Scholar 

  • Startzman RA, Kuo T-B (1987) A rule-based system for well log correlation. SPE Form Eval 2:311–319. https://doi.org/10.2118/15295-PA

    Article  Google Scholar 

  • Van Wagoner JC, Mitchum RM, Campion KM, Rahmanian VD (1990) Siliciclastic sequence stratigraphy in well logs, cores, and outcrops: concepts for high-resolution correlation of time and facies. American Association of Petroleum Geologists, Tulsa

    Google Scholar 

  • Weill P, Cojan I, Ors F, Rivoirard J, Beucher H (2013) Process-based modelling of a meandering fluvial reservoir: FLUMY and the Miocene Loranca Basin. In: Presented at the ICFS

  • Wilde A, Hill EJ, Schmid S, Taylor WR (2017) Wavelet tessellation and its application to downhole gamma data from the Manyingee and Bigrlyi sandstone-hosted uranium deposits

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Acknowledgements

This study is part of the first author’s Ph.D. thesis. The authors wish to thank Hélène Beucher for her valuable review. This method has been implemented as a well analysis tool in the Flumy® software within the scope of the Flumy Research Program. The authors are grateful to ENGIE (Neptune Energy) and ENI partners for support and fruitful discussions.

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Correspondence to Fabien Ors.

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Bubnova, A., Ors, F., Rivoirard, J. et al. Automatic Determination of Sedimentary Units from Well Data. Math Geosci 52, 213–231 (2020). https://doi.org/10.1007/s11004-019-09793-w

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  • DOI: https://doi.org/10.1007/s11004-019-09793-w

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