Neural Networks and Genetic Algorithms for Well Logging Inversion

• Main Author: 余勝棟
• Other Authors: 黃國源
• Format: Others
• Language: en_US
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/70700787165613620763

碩士 === 國立交通大學 === 資訊學院碩士在職專班資訊組 === 95 === In well logging inversion problem, a non-linear mapping exists between the synthetic logging measurements and the true formation conductivity. Without complexity of theoretic computation, neural network is able to approximate the input-output mapping through training with the iterative adjustment of connection weights. In our study, we develop the higher-order feature neural nets on the basis of neural network, and then apply on well logging inversion. The usually used training algorithm for neural network is gradient descent, which is easy to get trapped at local minimum, so we adopt a method that combine with genetic algorithm to improve the training efficiency. In addition, the convergence of gradient descent is slow, so we adopt the conjugate gradient to speed up the convergence. In order to make network more non-linear, we proposed higher-order feature neural nets that use functions to expand the input feature to higher degree. In order to use more training patterns and increase the convergence efficiency, we test various network architectures that use different number of input nodes. Besides, the experimental results show that the convergence efficiency of the network with 1 hidden layer is better than that without hidden layer, so we adopt the network with 1 hidden layer. We use 31 synthetic logging datasets. Each has 200 input features and corresponding outputs. The performance of network is evaluated by comparing the mean absolute error between the actual outputs and desired outputs. Leave-one-out validation method is used in experiments. Each time 30 datasets are used in training, the trained network is then tested with the left 1 dataset. After 31 trials, the network performance is computed by averaging these testing results. To validate the effectiveness of higher-order feature neural nets, the network size is 30-36-10 (not include bias), we train the network using conjugate gradient with synthetic logging datasets, and the trained network is then tested with real field logs. Results obtained from our experiments have shown that the proposed higher-order feature neural nets can be used effectively to process the well logging inversion. Our study shows an effective architecture of neural network to apply on well logging data inversion.