Machine-Learning-Assisted Segmentation of FIB-SEM Images with Artifacts for Improved of Pore Space Characterization of Tight Reservoir Rocks
Kirill Simonov
The focused-ion-beam scanning electron microscopy (FIB-SEM) technology allows imaging of nano-porous tight reservoir rock samples in 3D at a resolution up to 3 nm/voxel. However, the quality and efficient segmentation of FIB-SEM images is still a complicated and challenging task. Correct porosity determination from FIB-SEM images requires fast and robust segmentation. Typically, a trained operator spends days/week for subjective and semi-manual labeling of a single FIB-SEM dataset. The presence of FIB-SEM artifacts, such as pore backs, requires the development of a new methodology for efficient image segmentation. We developed a robust approach for an automated highly-efficient multimodal segmentation of FIB-SEM datasets using machine-learning (ML) based methods.
A representative collection of rocks samples was formed based on the petrophysical interpretation of well logs for a complex tight gas reservoir rock of the Berezov formation (West Siberia, Russia). The core samples passed through a multiscale imaging workflow for pore space structure upscaling from nanometer to log scale. FIB-SEM imaging resolved the finest scale using FEI Versa 3D DualBeam analytical system. Image segmentation utilized an architecture based on a convolutional neural network (CNN) in the DeepUnet configuration. The implementation utilized the Pytorch framework in a Linux environment. Computation exploited high-performance computing system based on Intel(R) Core (TM) i7-6700 CPUs and NVIDIA GTX 1080i TitanBlack GPUs.
The target data included three 3D FIB-SEM datasets with a physical size of around 20 × 15 × 25 μm with a voxel size of 5 nm. A professional geologist manually segmented (labeled) a fraction of slices. We split the labeled slices into training (TD) and validation data (VD). We then augmented training data to increase TD size. The developed CNN delivered promising results. The model performed automatic segmentation with the following average quality indicators based on VD: accuracy of 96.66%, the precision of 91.67%, recall of 67.57%, and F1 score of 77.00%. We achieved a significant boost in segmentation speed of 14.5 Mpx/min. compared to 0.18–1.45 Mpx/min. for manual labeling, yielding at least 10 times of efficiency increase.
The presented research work improves the quality of quantitative petrophysical characterization of complex reservoir rocks using digital rock imaging. The development allows the multiphase segmentation of 3D FIB-SEM data complicated with artifacts. It delivers correct and precise pore space segmentation resulting in insignificant turn-around time saving, as well as increased quality of porosity data. Although image segmentation using CNNs is a mainstream in the modern ML world, it is an emerging novel approach for reservoir characterization tasks.
