Spatial pseudo-labeling for semi-supervised facies classification

Abstract

For the Quantitative classification of facies is crucial to link seismic data with its corresponding lithology for the evaluation of reservoir properties. During the past decade, seismic volumes have increased to the degree that it is perplexing for the geoscientists and seismic interpreters to examine every single seismic section to carry out facies classification. This has triggered and inspired the need and development of machine learning approaches to efficiently predict seismic facies. However, classified data or labeled data (i.e., well log data) are limited by various restraints and needs huge amount of time and cost to obtain, whereas unlabeled data (seismic data) are in abundance and relatively easy to obtain. Geoscientists are tasked with interpreting large amounts of unlabeled data using very little labeled data. In this paper, we adopt a semi-supervised machine learning approach utilizing pseudo-labeling technique to predict seismic facies and to account for the scarcity of training data by taking advantage of unlabeled data. In each iteration, a small amount of unlabeled data is labeled in spatial manner and the most confident labels (labels that are within 95% confidence interval) are added back to the training data set, starting from labeling the unlabeled data adjacent to the well and spatially moving away in small steps (few traces in each step). The labels obtained from the unlabeled data near the well (pseudo-labels) are added to the labeled data to retrain the deep learning model (classifier) in each iteration. This addition of spatial pseudo-labels adds invariance and diversity to the classifier which in turn helps classifier capitalize on known input variance, which might act as a helpful prior knowledge for next prediction. In other words, the proposed workflow accounts for spatial change in lithology that occurs while moving away from the well. Using our proposed workflow, we observed that, the classification accuracy of a machine learning model trained on a small amount of training data can be enhanced considerably. Pseudo-labels also helped improve lateral continuity of facies. Furthermore, the proposed method outperformed conventional methods, achieving a test accuracy of 96.7% and logarithmic loss as low as 0.15 compared to 91% and 0.6, respectively for supervised baseline. Whereas, for training and validation; the training accuracy, validation accuracy and loss were 98.9%, 99.09% and 0.029 respectively.