Forming a new small sample deep learning model to predict total organic carbon content by combining unsupervised learning with semisupervised learning

The total organic carbon (TOC) content is a parameter that is directly used to evaluate the hydrocarbon generation capacity of a reservoir. For a reservoir, accurately calculating TOC using well logging curves is a problem that needs to be solved. Machine learning models usually yield the most accurate results. Problems of existing machine learning models that are applied to well logging interpretations include poor feature extraction methods and limited ability to learn complex functions. However, logging interpretation is a small sample problem, and traditional deep learning with strong feature extraction ability cannot be directly used; thus, a deep learning model suitable for logging small sample features, namely, a combination of unsupervised learning and semisupervised learning in an integrated DLM (IDLM), is proposed in this paper and is applied to the TOC prediction problem. This study is also the first systematic application of a deep learning model in a well logging interpretation. First, the model uses a stacked extreme learning machine sparse autoencoder (SELM-SAE) unsupervised learning method to perform coarse feature extraction for a large number of unlabeled samples, and a feature extraction layer consisting of multiple hidden layers is established. Then, the model uses the deep Boltzmann machine (DBM) semisupervised learning method to learn a large number of unlabeled samples and a small number of labeled samples (the input is extracted from logging curve values into SELM-SAE extracted features), and the SELM-SAE and DBM are integrated to form a deep learning model (DLM). Finally, multiple DLMs are combined to form an IDLM algorithm through an improved weighted bagging algorithm. A total of 2381 samples with an unlabeled logging response from 4 wells in 2 shale gas areas and 326 samples with determined TOC values are used to train the model. The model is compared with 11 other machine learning models, and the IDLM achieves the highest precision. Moreover, the simulation shows that for the TOC prediction problem, when the number of labeled samples included in the training is greater than 20, even if this number of samples is used to train 10 hidden layer IDLMs, the trained model has a very low overfitting probability and exhibits the potential to exceed the accuracies of other models. Relative to the existing mainstream shallow model, the IDLM based on a DLM provides the most advanced performance and is more effective. This method implements a small sample deep learning algorithm for TOC prediction and can feasibly use deep learning to solve logging interpretation problems and other small sample set problems for the first time. The IDLM achieves high precision and provides novel insights that can aid in oil and gas exploration and development. (C) 2019 Elsevier B.V. All rights reserved.