In addition to the ongoing development, pre-salt carbonate reservoir characterization remains a challenge, primarily due to inherent geological particularities. These challenges stimulate the use of well-established technologies, such as artificial intelligence algorithms, for image classification tasks. Therefore, this work intends to present an application of deep learning techniques to identify lithological patterns in Brazilian pre-salt carbonate rocks using microtomographic images. Four convolutional neural network models were proposed. The first model includes three convolutional layers, followed by a fully connected layer. This model is used as a base model for the following proposals. In the next two models, we replace the max pooling layer with a spatial pyramid pooling and a global average pooling layer. The last model uses a combination of spatial pyramid pooling followed by global average pooling in place of the final pooling layer. All models are compared using original images, when possible, as well as resized images. The dataset consists of 6,000 images from three different classes. The model performances were evaluated by each image individually, as well as by the most frequently predicted class for each sample. According to accuracy, Model 2 trained on resized images achieved the best results, reaching an average of 75.54% for the first evaluation approach and an average of 81.33% for the second. We developed a workflow to automate and accelerate the lithology classification of Brazilian pre-salt carbonate samples by categorizing microtomographic images using deep learning algorithms in a non-destructive way.

除了进行中的开发之外，主要由于固有的地质特殊性，盐下碳酸盐储层的表征仍然是一个挑战。这些挑战刺激了将成熟的技术（例如人工智能算法）用于图像分类任务。因此，这项工作旨在提出深度学习技术的应用，以使用显微断层图像识别巴西预盐碳酸盐岩中的岩性模式。提出了四种卷积神经网络模型。第一个模型包括三个卷积层，然后是一个完全连接的层。该模型用作以下提议的基础模型。在接下来的两个模型中，我们将最大池化层替换为空间金字塔池化和全局平均池化层。最后一个模型结合了空间金字塔池和全局平均池的组合，以代替最终池层。如果可能，将使用原始图像以及调整大小后的图像比较所有模型。数据集包含来自三个不同类别的6,000张图像。分别通过每个图像以及每个样本的最经常预测的类别来评估模型性能。根据准确性，在调整大小的图像上训练的模型2达到了最佳效果，第一种评估方法的平均值达到75.54％，第二种评估方法的平均值达到81.33％。我们开发了一种工作流程，以通过使用无损方式的深度学习算法对显微断层图像进行分类，来自动化和加速巴西预盐碳酸盐样品的岩性分类。