Abstract: Recently deep learning of artificial intelligence demonstrates certain advantages for seismic processing, interpretation and inversion. Previous studies show that the combination of deep learning and seismic inversion could generate more robust results than traditional methods. The deep learning technique could achieve results of high resolution which is critical for reservoir development. This paper investigates the effects of different training datasets used in seismic inversion based on Convolutional Neural Network (CNN) by designing a reservoir model and its corresponding seismic response. The result shows that the prediction accuracy of this Neural Network increases with the increase of training datasets size in a certain range. The relationship of inversion quality and ratio between the entire datasets and the training data is demonstrated. In addition, different levels of noises in seismic are tested for CNN training. The results demonstrate the generalization and anti-noise ability of the designed CNN.

Key words: artificial intelligence, deep learning, convolutional neural network, seismic inversion, geological modeling