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

Abstract

S U M M A R YA 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.

Interval inversion approach for an improved interpretation of well logs

“…They can be a priori given from manual analysis of the GR or shallow resistivity logs, determined by multivariate statistical methods, such as cluster analysis, fuzzy logic, and neural networks (Maiti et al, 2007), or they can be estimated automatically by interval inversion . In solving the inverse problem, we apply two different approaches.…”

Bayesian neural network modeling of tree-ring temperature variability record from the Western Himalayas

“…The processing elements/nodes are interconnected layer by layer and the function of an each node is determined by the connections weights, biases and the structure of the network (Bishop, 1995;Poulton, 2001). Detailed sequential developments of the ANN methods are available elsewhere (Poulton, 2001;Maiti et al, 2007;Maiti and Tiwari, 2010b). In the popular back-propagation method, an error is usually minimized by adjusting weights and biases via the gradient based iterative chain rule from the output layer to the input layer (Rumelhart et al, 1986).…”

“…It is slow and often gets trapped in one of the many local minima of complex error surface. Consequently to evade this problem, many improvements have been put forward, such as the inclusion of momentum variable, adaptive learning rate (Poulton, 2001;Maiti et al, 2007), conjugate gradient (Bishop, 1995), scaled conjugate gradient (Bishop, 1995;Maiti and Tiwari, 2010b), Levenberg-Marquaard (Bishop, 1995;Poulton, 2001).…”

Inversion of Schlumberger resistivity sounding data from the critically dynamic Koyna region using the Hybrid Monte Carlo-based neural network approach

“…For example: (1) for seismic event classification (Dystart and Pulli, 1990), (2) well log analysis (Aristodemou et al, 2005;Maiti et al, 2007;Maiti and Tiwari, 2007, 2010b, (3) first arrival picking (Murat and Rudman, 1992), (4) earthquake prediction (Feng et al, 1997), (5) inversion (Raiche, 1991;Devilee et al, 1999), (6) parameter estimation in geophysics (Macias et al, 2000), (7) prediction of aquifer water level (Coppola Jr. et al, 2005), (8) magneto-telluric data inversion (Spichak and Popova, 2000), (9) magnetic interpretations (Bescoby et al, 2006), (10) signal discrimination (Maiti and Tiwari, 2010a), (11) modeling (Sri Lakshmi and Tiwari, 2009), (12) DC resistivity inversion (Qady and Ushijima, 2001;Lampinen and Vehtari, 2001;Singh et al, 2005Singh et al, , 2006Singh et al, , 2010.…”