Cluster defined sedimentary elements of deep-water clastic depositional systems and their 3D spatial visualization using parametrization: a case study from the Pannonian-basin

Main Article Content

Janina Horvath
Szabolcs Borka
János Geiger

Abstract

Many multivariate statistical techniques have the ability to handle large data sets or a great number of parameters. Therefore, these multivariate statistical approaches are widely used in clastic sedimentology for facies analysis. Furthermore, most of the techniques which try to separate more or less homogeneous subsets can be subjective. This subjectivity raises several questions about the significance and confidence of clustering. The goal of this study is to optimize clustering and to evaluate the proper number of clusters needed in order to describe sedimentary and lithological facies through common characteristics. Also, with the interpretation of the clusters, the parametrized geometry adds further but quasi-subjective information to a 3D geologicalmodel. Two assumptions must be met: (1) well-definable geometries must correspond to the architectural elements (2) it is assumed that exactly one sedimentary or lithological facies belongs to each structural element and the flow properties are determined by these structural elements. This approach was applied to the clastic depositional data from a Miocene hydrocarbon reservoir (Algyő field, Hungary) to demonstrate the fidelity of the clustering method yielding an optimum of five cluster facies. The revealed clusters represent lithological characteristics within a (delta fed) submarine fan system. The paper deals with two stressed clusters in particular, showing sinusoid channels which were recognizable and measureable using parametrisation.

Downloads

Download data is not yet available.

Article Details

Section
Original Scientific Papers

References

ASANTE, J. & KREAMER, D. (2015): A New Approach to Identify Recharge Areas in the Lower Virgin River Basin and Surrounding Basins by Multivariate Statistics.– Mathematical Geosciences, 47/7, 819–842. doi: 10.1007/s11004-015-9583-0

BÉRCZI, I. (1988): Preliminary sedimentological investigation of a Neogene Depression in the Great Hungarian Plain.– In: ROYDEN, L.H., & HORVÁTH, F. (eds.): The Pannonian Basin: A study in basin evolution, AAPG Memoir, 45, 107–116.

BORKA, SZ. (2016): Markov chains and entropy tests in genetic-based lithofacies analysis of deep-water clastic depositional systems.– Open Geosci., 8, 45–51. doi: 10.1515/geo-2016-0006

BOX, G.E.P. & COX, D.R. (1964): An analysis of transformations, Journal of the Royal Statistical Society, Series B, 26, 211–252.

CALINSKI, T. & HARABASZ, J. (1974): A dendrite method for cluster analysis, Communications in Statistics, 3, No. 1, 1–27. doi: 10.1080/03610927408827101

GAN, G., MA, C. & WU, J. (2007): Data Clustering: Theory, Algorithms, and Applications, Society for Industrial and Applied Mathematics (SIAM), Philadelphia, Pennsylvania, 466 p. doi: 10.1137/1.9780898718348

GRUND, SZ., & GEIGER, J. (2011): Sedimentologic modelling of the Ap-13 hydrocarbon reservoir, Central European Geology, 54/4, 327–344. doi: 10.1556/CEuGeol.54.2011.4.2

HARTIGAN, J.A. (1975): Clustering Algorithms– John Wiley and Sons, Inc., NY, USA, 351 p.

HORVÁTH, J. (2015): Depositional facies analysis in clastic sedimentary environments based on neural network clustering and probabilistic extension.– Unpubl. PhD Thesis, University of Szeged, 118 p.

HORVÁTH, J. & MALVIĆ, T. (2013): Characterization of clastic sedimentary environments by clustering algorithm and several statistical approaches – case study, Sava Depression in Northern Croatia.– Central European Geology, 56/4, 281–296.

MAHARAJA, A. (2008): TiGenerator: Object-based training image generator, Computers and Geosciences.– Elsevier, 34, 1753–1761. doi: 10.1016/j.cageo.2007.08.012

MUTTI, E. (1985): Turbidite systems and their relations to depositional sequences.– In: ZUFFA, G.G. (ed.): Provenance of Arenites. D. Reidel Publishing Company, 65–93. doi: 10.1007/978-94-017-2809-6_4

NORMARK, W.R. (1970): Growth patterns of deep sea fans. AAPG Bulletin, 54, 2170–2195.

PYRCZ, M.J., BOISVERT , J.B. & DEUTSCH, C.V. (2008): A library of training images for fluvial and deepwater reservoirs and associated code. Computers and Geosciences, Elsevier, 34, 542–560. doi: 10.1016/j.cageo.2007.05.015

PYRCZ, M.J. & DEUTSCH, C.V. (2014): Geostatistical reservoir modelling.– Oxford University Print, 2nd edition, University of Oxford, 448 p.

READING, H.G. & RICHARDS, M. (1994): Turbidite systems in deep-water basin margins classified by grain size and feeder system.– AAPG Bulletin, 78, 792–822.

SAKIA, R.M. (1992): The Box-Cox transformation technique: a review.– The Statistician, 41, 169–178. doi: 10.2307/2348250

SHANMUGAM, G. (2006): Deep-Water Processes and Facies Models: Implications for Sandstone Petroleum Reservoirs.– Elsevier, 1st ed., Amsterdam, The Netherlands, 498 p.

TEMPL, M., FILZMOSER, P. & REIMANN, C. (2006): Cluster analysis applied to regional geochemical Data: problems and possibilities.– Research report-CS-2006-5, Vienna University of Technology, 39 p.