The identification of micropore systems in carbonate rocks is an important task of image processing because of the high impact these systems cause on fluid flow. Currently, one of the main tools used to characterize rock samples is computed tomography (CT). Such micro information poses the challenge associated with the limitation of the CT’s resolution. Therefore, we propose an alternative method of inserting the micropore features of μ CT to lower resolution images, but with higher coverage. We can perform this by a novel application of Style Transfer that can insert the heterogeneity pattern of high-resolution (HR) images (CT of 7 and 40 μm resolution) into low-resolution (LR) images (CT of 90 μm resolution), downscaling the image through a super-resolution method. This technique uses the power of VGG19, a convolutional neural network that won the ImageNet Large-Scale Visual Recognition Challenge in 2014, as a texture extractor. We applied this novel technique to condensed shell rocks, called coquinas, from the Itapema Formation, Santos Basin offshore Brazil. The porosity of the LR image, with initial average value of 11%, resulted in an average porosity of 12% (40 μm res.) and 13% (7 μm res.) after downscaling. This is closer to the porosity range of the coquina (13% to 32%, with a mean of 21%) and an increase in the porosity of 6% and 19% in average, respectively. Despite this, the connectivity of the original LR CT was of 3% on average and, in the simulated HR CT, the connectivity was of 5% (40 μm res.) and 6% (7 μm res.). In addition, in such examples, this method inserted connectivity in directions that were null in low-resolution images before the style transfer. Hence, the results demonstrated that the Style Transfer offers an alternative for downscaling CT images by inserting the texture from high-resolution images.

碳酸盐岩中的微孔系统的识别是图像处理的重要任务，因为这些系统对流体流动产生很大的影响。当前，用于表征岩石样品的主要工具之一是计算机断层扫描（CT）。这种微信息带来了与CT分辨率限制相关的挑战。因此，我们提出的插入微孔特征的另一种方法μ CT到较低分辨率的图像，但具有更高的覆盖率。我们可以通过式转换的一个新的应用，可以插入高分辨率（HR）图像的异质性图案（7和40的CT执行此μ米分辨率）转换成低分辨率（LR）图像（CT 90的μm分辨率），通过超分辨率方法缩小图像的尺寸。该技术使用了卷积神经网络VGG19的功能作为纹理提取器，该卷积神经网络在2014年赢得了ImageNet大规模视觉识别挑战赛的冠军。我们将这种新技术应用于巴西近海桑托斯盆地Itapema组的凝结的贝壳岩石，称为coquinas。LR图像的孔隙率，11％的初始平均值，导致了12％的平均孔隙率（40个μ米水库）和13％（7 μ缩小后）。这更接近于Coquina的孔隙率范围（13％到32％，平均为21％），平均孔隙率分别增加了6％和19％。尽管如此，原始LR CT的连通性平均为3％，而在模拟HR CT中，连通性为5％（40μm分辨率）和6％（7μm分辨率）。另外，在此类示例中，此方法在样式转换之前在低分辨率图像中为零的方向上插入了连通性。因此，结果表明，通过从高分辨率图像插入纹理，样式转换为缩小CT图像提供了一种替代方法。