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首页> 外文期刊>Applied Soft Computing >Forming a new small sample deep learning model to predict total organic carbon content by combining unsupervised learning with semisupervised learning
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Forming a new small sample deep learning model to predict total organic carbon content by combining unsupervised learning with semisupervised learning

机译：形成一个新的小样本深度学习模型，通过组合无监督学习来预测总有机碳含量

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摘要

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.
机译：总有机碳（TOC）含量是直接用于评估储层的烃的发电能力的参数。对于储层，使用良好的测井曲线准确计算TOC是需要解决的问题。机器学习模型通常会产生最准确的结果。应用于井井料解释的现有机器学习模型的问题包括差的特征提取方法和学习复杂功能的有限能力。然而，测井解释是一个小的样本问题，传统的深度学习具有强大的特征提取能力不能直接使用;因此，在本文中提出了一种适合记录小样本特征的深度学习模型，即在集成的DLM（IDLM）中，在集成的DLM（IDLM）中，应用于TOC预测问题。本研究也是在井测井解释中的深度学习模型的第一次系统应用。首先，该模型使用堆叠的极端学习机稀疏的AutoEncoder（Selm-SAE）无监督的学习方法来执行大量未标记的样本来执行粗糙的特征提取，并且建立由多个隐藏层组成的特征提取层。然后，该模型使用深层Boltzmann机器（DBM）半培育的学习方法来学习大量未标记的样本和少量标记的样本（从记录曲线值提取输入到SELM-SAE提取的特征）和SELM -SAE和DBM集成以形成深度学习模型（DLM）。最后，将多个DLMS组合以通过改进的加权袋算法形成IDLM算法。共有2381个样品，其中2个页岩气区域的4个孔和具有确定的TOC值的326个样品的4个井的未标记测井响应用于培训模型。该模型与11个其他机器学习模型进行比较，并且IDLM实现了最高精度。此外，模拟显示，对于TOC预测问题，当培训中包含的标记样本的数量大于20时，即使使用该数量的样本用于训练10个隐藏层IDLMS，训练型的模型具有非常低的过度概率并表现出超过其他模型的准确性的潜力。相对于现有的主流浅模型，基于DLM的IDLM提供了最先进的性能，更有效。该方法实现了一个小型样本深度学习算法，用于TOC预测，可以是第一次求解Lealging解释问题和其他小样本集问题的深度学习。 IDLM实现了高精度，并提供了可以帮助石油和天然气勘探和发展的新颖见解。 （c）2019年Elsevier B.V.保留所有权利。

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