No abstract

Note: This article is part of the JCP Special Topic on Electronic Structure Software.

To make efficient use of image-based rock physics workflow, it is necessary to optimize different criteria, among which: quantity, representativeness, size and resolution. Advances in artificial intelligence give insights of databases potential. Deep learning methods not only enable to classify rock images, but could also help to estimate their petrophysical properties. In this study we prepare a set of thousands highresolution 3D images captured in a set of four reservoir rock samples as a base for learning and training. The Voxilon software computes numerical petrophysical analysis. We identify different descriptors directly from 3D images used as inputs. We use convolutional neural network modelling with supervised training using TensorFlow framework. Using approximately fifteen thousand 2D images to drive the classification network, the test on thousand unseen images shows any error of rock type misclassification. The porosity trend provides good fit between digital benchmark datasets and machine learning tests. In a few minutes, database screening classifies carbonates and sandstones images and associates the porosity values and distribution. This work aims at conveying the potential of deep learning method in reservoir characterization to petroleum research, to illustrate how a smart image-based rock physics database at industrial scale can swiftly give access to rock properties.

The introduction of Ceramic Matrix Composites (CMCs) for aircraft engines gas turbine components aims at increasing the turbine performance and in-service life on the one hand, and on the other hand saving weight, hence reducing fuel consumption. Material health control of turbine parts is an essential procedure, given the criticality of their operating performance. X-ray tomography is a particularly suitable technique for non destructive testing. However, the images produced by this 3D imaging method can be very large, making computations very time- and memory consuming, especially for characterizing complex porous materials such as CMCs. Voxaya SAS and IRT Saint-Exupery designed and tested a specific non destructive testing workflow based on Voxaya’s software, Voxilon, for the analysis of CMC engine internal parts from X-ray tomographic images. We present how Voxilon’s innovative architecture and rapid algorithms allows swiftly processing a large number of tomographic images and automatizing the sensitivity analysis of input parameters.