Unsupervised contrastive learning for few-shot TOC prediction and application.

Total organic carbon (TOC) content is significant for "sweet spots" prediction and resource calculation. Traditional physical methods and machine learning methods, however, only use labeled samples (core TOC samples and well logging data) to predict TOC, resulting in a performance bottleneck. Meanwhile, many oilfields have considerable unlabeled data (well logging data). Therefore, this paper proposed a pretrain-and-finetune few-shot learning framework, called Contrastive Learning-Convolutional Neural Network (CL-CNN), to predict TOC using unlabeled data. CL has been a research hotspot in unsupervised learning in recent years, with the principle of reducing the distance between similar samples and increasing the distance between different samples in the feature space. CNN is distinguished by its strong generalization ability and high prediction accuracy, as well as its local connectivity and weight sharing. The CL-CNN model first uses CL to pretrain the initial parameters of the CNN model on a large number of unlabeled datasets and then finetunes the resultant CNN model using the labeled data. The framework combines the advantages of CL and CNN to further improve the prediction accuracy and generalization ability. The proposed method was applied to predict the TOC of shale gas in the Sichuan Basin, China, using 121 core samples and 1224 well logging data from the JY 10 well (121 logging data for supervised learning and 1103 logging data for unsupervised learning). Besides, JY 10–10 well data were used to validate the CL-CNN model. Experimental results demonstrated that the CL-CNN model outperformed many machine learning methods, including the support vector machine (SVM), Gaussian process regression (GPR), random forest (RF), and CNN models, indicating the feasibility and effectiveness of the proposed method. Because of this, the CL-CNN model can also be used as a general prediction tool for reservoir parameters such as porosity and permeability. • Contrastive learning was used to learn the general attributes of the unlabeled data. • Contrastive learning-convolutional neural network (CL-CNN) was proposed to predict TOC content. • Unsupervised pretraining and supervised finetuning were used for few-shot TOC prediction. • CL-CNN obtained promising results for predicting TOC and outperformed many methods. • The plane distribution of TOC in the study area was predicted by CL-CNN accurately. [ABSTRACT FROM AUTHOR]