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Neural network stochastic simulation applied for quantifying uncertainties

Abstract : Generally the geostatistical simulation methods are used to generate several realizations of physical properties in the sub-surface, these methods are based on the variogram analysis and limited to measures correlation between variables at two locations only. In this paper, we propose a simulation of properties based on supervised Neural network training at the existing drilling data set. The major advantage is that this method does not require a preliminary geostatistical study and takes into account several points. As a result, the geological information and the diverse geophysical data can be combined easily. To do this, we used a neural network with multi-layer perceptron architecture like feed-forward, then we used the back-propagation algorithm with conjugate gradient technique to minimize the error of the network output. The learning process can create links between different variables, this relationship can be used for interpolation of the properties on the one hand, or to generate several possible distribution of physical properties on the other hand, changing at each time and a random value of the input neurons, which was kept constant until the period of learning. This method was tested on real data to simulate multiple realizations of the density and the magnetic susceptibility in three-dimensions at the mining camp of Val d'Or, Québec (Canada).
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Nacim Foudil-Bey, Jean-Jacques Royer, Li Cheng, Fouad Erchiqui, Jean-Claude Mareschal. Neural network stochastic simulation applied for quantifying uncertainties. The international journal of multiphysics., Multi-Science Publishing, 2013, 7 (1), pp.31 - 40. ⟨10.1260/1750-9548.7.1.31⟩. ⟨hal-01763615⟩



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