Total organic carbon content prediction of shale reservoirs based on discrete process neural network

Key words:  total organic carbon  discrete process neural network  network training  Moore-Penrose generalized inverse

Abstract:

Traditional methods in TOC fitting generally have low precision due to the effects of lithology change. In order to improve TOC fitting precision and to reduce the time cumulative error for continuous signals in the artificial neuron network, an extreme learning discrete process neural network is proposed. A vector form is used to simulate the process input in the model. The time domain aggregation for discrete data input is controlled by the parabolic interpolation using numerical integration in the discrete process neuron. Through analysis of structure of discrete process neuron, an extreme learning algorithm is proposed. The parameters of the hidden layer are randomly assigned and the Moore-Penrose generalized inverse is used to compute the output weights. The method is applied to TOC fitting and prediction usingsome logging curves which have most sensitive response for TOC. The TOC fitting results are compared with the traditional methods and other neural network. The results show that the proposed method has higher fitting precision and faster learning speed, and the predicted TOC and actual TOC have better correlations.