Neural network modelling and classification of lithofacies using well log data: a case study from KTB borehole site

Details Neural network modelling and classification of lithofacies using well log data: a case study from KTB borehole site Neural network modelling and classification of lithofacies using well log data: a case study from KTB borehole site

May 2007

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2007geoji.169..733m&link_type=abstract

Geophysical Journal International, Volume 169, Issue 2, pp. 733-746.

Physics

4

Ann, Back Propagation Method, Ktb Boreholes, Lithofacies, Petrophysics, Well Log

Scientific paper

A novel approach based on the concept of super self-adapting back propagation (SSABP) neural network has been developed for classifying lithofacies boundaries from well log data. The SSABP learning paradigm has been applied to constrain the lithofacies boundaries by parameterzing three sets of well log data, that is, density, neutron porosity and gamma ray obtained from the German Continental Deep Drilling Project (KTB). A multilayer perceptron (MLP) neural networks model was generated in a supervised feed-forward mode for training the published core sample data. A total of 351 pairs of input and output examples were used for self-adaptive network learning and weight and bias values were appropriately updated during each epoch according to the gradient-descent momentum scheme. The actual data analysis suggests that the SSABP network is able to emulate the pattern of all three sets of KTB data and identify lithofacies boundaries correctly. The comparisons of the maximum likelihood geological sections with the available geological information and the existing geophysical findings over the KTB area suggest that, in addition to the known main lithofacies boundaries units, namely paragneisses, metabasites and heterogeneous series containing partly calc-silicate bearing paragneisses-metabasites and alternations of former volcano-sedimentary sequences, the SSABP neural network technique resolves more detailed finer structures embedded in bigger units at certain depths over the KTB region which seems to be of some geological significance. The efficacy of the method and stability of results was also tested in presence of different levels of coloured noise. The test results suggest that the designed network topology is considerably unwavering for up to 20 per cent correlated noise; however, adding more noise (~50 per cent or more) degrades the results. Our analyses demonstrate that the SSABP based approach renders a robust means for the classification of complex lithofacies successions from the KTB borehole log data and thus may provide useful guide/information for understanding the crustal inhomogeneity and structural discontinuity in many other regions.

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