Defining depositional environment by using neural network

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Janina Horvath

Abstract

Traditional techniques to identify a depositional body from core data are costly and sometimes difficult to extrapolate to uncored wells. The application of Kohonen’s Self Organized Map (SOM) approach may be useful for the interpretation of a depositional rock body through well-log data. SOM is based on a clustering algorithm and this method can be used to discover spatial patterns occurring as clusters in unstructured data sets. An example of the application of SOM is presented whereby clusters through SOM can indicate the contours of well-known depositional patterns such as sub-environments.

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Original Scientific Papers

References

AHMED, T., LINK, C.A., PORTER, K.W., WIDEMAN, C.J., HIM-MER, P. & BRAUN, J. (1997): Application of Neural Network Pa-rameter Prediction in Reservoir Characterization and Simulation – A Case History: The Rabbit Hills Field – paper SPE 38985 presented at the 1997 SPE Latin American and Caribbean Petroleum Engineering Conference and Exhibition held in Rio de Janeiro, Bra zil, 30 August–3 September 1997. doi: 10.2118/38985-MS

AKINYOKUN, O.C., ENIKANSELU, P.A., ADEYEMO, A.B. & ADE-SIDA, A. (2009): Well Log interpretation model for the determina-tion of lithology and fl uid contents.– The Pacific Journal of Science and Technology, Springer.

BÉRCZI, I. (1988): Preliminary sedimentological investigations of a Ne-ogene depression in the great Hungarian Plain.– In: ROYDEN & HORVÁTH (ed.): The Pannonian basin – a study in basin evolu-tion.– AAPG Memoir, 45, 107–116.

CHANG, H.-C., KOPASKA-MERKEL, D.C. & CHEN, H.-C. (2002): Identification of lithofacies using Kohonen self-organizing maps.– Computers & Geosciences, 28, 223–229. doi: 10.1016/S0098-3004-(01)00067-X

FAUSETT, L. (1994): Fundamentals of Neural Networks – Architectures, Algorithms and Applications, Prentice Hall, Englewood Cliffs, NJ.

GEIGER, J. (2005): Sedimentological study of Szőreg-1 reservoir (Algyő Field): a kind of combination of traditional and 3D sedimentologi-cal approaches.– In: HUM, L. et al. (eds.): Environmental Histori-cal Studies from the Late Tertiary and Quarternary of Hungary. De-partment of Geology and Paleontology, University of Szeged, 25–45.

GEIGER, J., KISSNÉ, V.K., VERES, K. & KOMLÓS, J. (1998): A Szőreg-1 telep 3D rezervoár geológiai modellje. (3D sedimento-logical modelling of Szőreg-1 reservoir), KUMMI Jelentés. p. 216. Tsz. 156–5636., Szeged.

HAYKIN, S. (1999): Neural Networks: a Comprehensive Foundation. Macmillan.– The Knowledge Engineering Review, New York, 13/4, 409–412. doi: 10.1017/S0269888998214044

JOHANSSON, J., JERN, M., TRELOAR, R., JANSSON M. (2003): “Visual Analysis based on Algorithmic Classifi cation” IV, Seventh International Conference on Information Visualization, p. 86.

KOHONEN, T. (1982): Self-organized formation of topologically correct feature maps.– Biological Cybernetics, 43, 59–69. doi: 10.1007/ BF00337288

KOHONEN, T. (2001): Self Organized Maps – Springer, Berlin, Germany.

LAMPINEN, T., LAURIKKALA, M., KOIVISTO, H. & HONKANEN, T. (2005): Profi ling Network Applications with Fuzzy C-means and Self-Organizing Maps Studies in Computational Intelligence, Volume 4/2005 Classification and Clustering for Knowledge Discovery, Springer Berlin / Heidelberg.

MAHALANOBIS, P.C. (1936): “On the generalized distance in statis-tics”. Proceedings of the National Institute of Sciences of India 2/1: 49–55.

PATTERSON, D. (1996) Artifi cial Neural Networks, Prentice Hall, Sin-gapore, xy p.

PETTIJOHN, F.J. & POTTER, P.E. (1972): Sand and sandstone.– Sprin ger Verlag Berlin – Heidelberg – New York, 618 p.

RÉVÉSZ, I. (1982): Az algyői Maros-Szőreg szénhidrogéntelepek üle-dékföldtani modellje – egy fosszilis delta fejlődéstörténete. (Modelling of Maros-Szőreg hydrocarbon reservoir (Algyő Field) – a develop history of the pannonian fossil delta.).– Kőolaj és Földgáz, 115, 176–177.

ROGERS, S.J., CHEN, H.C., KOPASKA-MERKEL, D.C. & FANG, J.H. (1995): Predicting Permeability from Porosity Using Artifi cial Neural Networks. – AAPG Bulletin, 79/12, 1786–1797.

ULTSCH, A. (1995): Self Organizing Neural Networks perform different from statistical k-means clustering.– In: Proc. GfKl, Basel, Swiss.

ULTSCH, A., KORUS, D. & WEHRMANN, A. (1995): Neural networks and their rules for classifi cation in marine geology, Raum und Zeit in Umweltinformations-systemen, 9th Intl. Symposium on Compu-ter Science for Environmental Protection CSEP ́95, Vol. I, ed. GI-Fachausschuß 4.6 “Informatik im Umweltschutz” vol. 7, Metropolis-Verlag, Marburg, 676–693.