LAUSR

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.