Semi-Supervised Learning Based on Generative Adversarial Network and Its Applied to Lithology Recognition

Abstract

Lithology recognition is an essential part of reservoir parameter prediction. Compared to conventional algorithms, deep learning that needs a large amount of training data as support can extract features automatically. In the process of real data acquisition, the labeled data account for only a small portion due to high drilling cost, and it is difficult to achieve the data size required for deep learning training, resulting in a significant variance of the recognition model. In this paper, for this shortage, a semi-supervised algorithm based on generative adversarial network (GAN) with Gini-regularization is proposed, called SGAN_G, which takes borehole-side data as labeled data and seismic data as unlabeled data. First, the SGAN_G is trained by Adam (a method for stochastic optimization) algorithm and utilizes a discriminator to lithology recognition. And, we add the entropy regularization to the initial loss function which enhances the convergence speed and accuracy of the model. Eventually, we propose a novel sampling approach which employs multiple sampling points of seismic data as inputs to use the stratum information implicitly. Through the experimental comparison with a variety of supervised approaches, we can see that the SGAN_G can achieve higher prediction accuracy by using unlabeled data effectively.