Machine-learning-based object detection in images for reservoir characterization: A case study of fracture detection in shales

Abstract

Imaging tools are widely used in the petroleum industry to investigate structural features of reservoir rocks directly at multiple scales. Quantitative image analysis is often used to determine various rock properties, but it requires significant time and effort, particularly to analyze a large number of samples. Automated object detection represents a potential solution to this efficiency problem. This method uses computers to efficiently provide quantitative information for thousands of images. Automated fracture detection in scanning electron microscope (SEM) images is presented as an example to show the workflow of using advanced deep-learning tools for quantitative rock characterization. First, an automatic object-detection method is presented for fast identification and characterization of microfractures in shales. Using this approach, we analyzed 100 SEM images obtained from deformed and intact samples of a carbonate-rich shale and a siliceous shale with the goal of analyzing the abundance and characteristics of microfractures generated during hydraulic fracturing. Most of the fractures are detected with about 90% success rate relative to manual picking. Second, we obtained statistics of length and areal porosities of these fractures. The experimentally deformed samples had slightly more detectable microfractures (1.8 fractures/image on average compared to 1.6 fractures/image), and the microfractures induced by shear deformation tend to be short (<50 μm) in the Eagle Ford and long in the siliceous samples, presumably because of differences in rock fabric. In future work, this approach will be applied to characterize the shape and size of mineral grains and to analyze relationships between fractures and minerals.

Workflow for high‐resolution phase segmentation of cement clinker from combined BSE image and EDX spectral data

“…After superpixel/regions were generated, each of these regions was treated as a homogeneous particle and was annotated to a corresponding class, which could be solved with the use of automatic machine learning classification algorithms such as random forest, 14 k‐nearest neighbours 14 or neural network 15 . However, machine learning algorithms require a large amount of training data/labelled data and to generate these data sets it is very time consuming, even with a state‐of‐the‐art manual labelling tool 16,17 …”

“…15 However, machine learning algorithms require a large amount of training data/labelled data and to generate these data sets it is very time consuming, even with a state-of-the-art manual labelling tool. 16,17 Results in Figure 2C and D compare the superpixels extracted by DT watershed as well as by the popular SLIC algorithm. 11 Both algorithms can capture most of the details in the background image.…”

Smart detection of fractures in formation image logs for enhanced CO2 storage

“…Tian and Daigle presented a machine learning framework for the identification of fractures in scanning electron microscope (SEM) images from carbonate-rich shale and siliceous shale samples. The framework utilizes the tensor flow object-detection API to detect objects in images, and utilizes single-shot detector in combination with a mobile net model (Tian and Daigle, 2018). While the results are promising for the detection of fractures in SEM images, the same technique may not easily be applied to formation image logs.…”

Real-time human detection in thermal infrared imaging at night using enhanced Tiny-yolov3 network

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