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A practical method for well log data classification. (English) Zbl 1453.86041
Summary: In this work, a method for well log data classification is presented. The method relies on a coordinate transformation to restructure the data in an optimal way and a quasi-probabilistic interpolation technique capable of smoothing noisy data. The approach does not require case-specific design, is computationally efficient and provides a statistical characterization of the classification problem. Consequently, transition zones between facies can be modelled in a realistic fashion and intermediate rock types can be identified with ease. Apart from being capable of classifying unseen data with high accuracy, the technique can also be used as an informative quality and consistency assessment tool for manually classified data. The properties of the method are demonstrated on a realistic test case study.
MSC:
86A32 Geostatistics
86-08 Computational methods for problems pertaining to geophysics
Software:
EGO
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References:
[1] Hall, B., Facies classification using machine learning, Lead. Edge, 35, 906-909 (2016)
[2] Dubois, MK; Bohling, GC; Chakrabarti, S., Comparison of four approaches to a rock facies classification problem, Comput. Geosci., 33, 599-617 (2007)
[3] Qi, L.; Carr, T., Neural network prediction of carbonate lithofacies from well logs, Big Bow and Sand Arroyo Creek fields, Southwest Kansas, Comput. Geosci., 32, 947-964 (2006)
[4] Saljooghi, B.; Hezarkhani, A., Comparison of wavenet and ann for predicting the porosity obtained from well log data, J. Pet. Sci. Eng., 123, 11 (2014)
[5] Sfidari, E., Kadkhodaie, A., Rahimpour-Bonab, H., Soltani, B.: A hybrid approach for litho-facies characterization in the framework of sequence stratigraphy: a case study from the south pars gas field, the persian gulf basin. Journal of Petroleum Science and Engineering, 121 (2014)
[6] Chehrazi, A.; Rezaee, R., A systematic method for permeability prediction, a Petro-Facies approach, J. Petroleum Sci. Eng., 82-83, 1-16 (2012)
[7] Hall, M.; prediction, BHall, Distributed collaborative: results of the machine learning contest, Lead. Edge, 36, 267-269 (2017)
[8] Imamverdiyev, Y., Sukhostat, L.: Lithological facies classification using deep convolutional neural network. Journal of Petroleum Science and Engineering, 174 (2018)
[9] Awrejcewicz, J., Krys’ko, V.A., Vakakis, A.F.: Order Reduction by Proper Orthogonal Decomposition (POD) Analysis, pp. 177-238. Springer, Berlin (2004)
[10] Hardy, R., Multiquadric equations of topography and other irregular surfaces, J. Geophys. Res. (1896-1977), 76, 8, 1905-1915 (1971)
[11] Jones, D.; Schonlau, M.; Welch, W., Efficient global optimization of expensive black-box functions, J. Glob. Optimi., 13, 455-492, 12 (1998) · Zbl 0917.90270
[12] Llanas, B.; Sainz, FJ, Constructive approximate interpolation by neural networks, J. Comput. Appl. Math., 188, 2, 283-308 (2006) · Zbl 1089.65012
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