Role of Mineralogy in Controlling Fracture Formation

Brunhoeber, Olivia; Beckingham, Lauren E.

doi:10.1130/abs/2021se-362134

Cited by 2 publications

(1 citation statement)

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“…This can be used to constrain or check transport model assumptions and provide a clearer understanding of the reactive origins of mineral textures, such as the suggestion above for sample 09IL06 that calcite precipitation may have filled initial porosity. Important information may also be obtained on deformation or other processes that depend on, or modify, these spatial relationships, such as the relationship between the mineralogical texture of a rock and fractures 136 or other physical phenomena (e.g., stylolites or other elements of rock fabric). In addition, the same approach can be used to quantify the relationships between chemical, as well as mineralogical patterns if binary images can be obtained for various concentration ranges of the elements of interest, or as a function of time.…”

Section: ■ Conclusionmentioning

confidence: 99%

A Quantitative Approach to the Analysis of Reactive Mineralogy and Surface Area

Anovitz

Beckingham

Sheets

et al. 2022

ACS Earth Space Chem.

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The textural patterns of geologic materials both reflect and control reactive mineralogy and transport. This is true for a wide range of geochemical reactions. The chemistry of fluids controlled by dissolution during transport depends on the mineralogy to which those fluids have access, either directly on pore walls or fractures, or through diffusive or grain boundary transport in the bulk rock. Similarly, mineral growth during metamorphic, igneous, and diagenetic processes and the final mineral distribution depends on the accessibility of the necessary components to the growing phases and thus on their spatial distribution. To either derive petrologic information from a given rock sample, or to predict fluid chemistry during reactive transport, the geometric relationships between the pore structure and mineralogy of the sample must be quantified. This paper describes a method, based on two-point autocorrelation/crosscorrelation analysis, by which the statistical distribution of distances between the phases, including the pore space, in a given sample may be quantified as the percentage likelihood that a given phase lies at a given distance from any other phase (or the pore space or pore boundary) in that sample, and suggests how those results can be used to quantify reactive mineralogy. This approach is not data source dependent; any two-or three-dimensional image can be analyzed. The results are limited only by the quality of the imagery. Analysis of three examples, one from the Mt. Simon Sandstone in the Illinois Basin and two from the Nagaoka CO 2 sequestration site in Japan, demonstrates the method.

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Section: ■ Conclusionmentioning

confidence: 99%

A Quantitative Approach to the Analysis of Reactive Mineralogy and Surface Area

Anovitz

Beckingham

Sheets

et al. 2022

ACS Earth Space Chem.

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Integrating Machine/Deep Learning Methods and Filtering Techniques for Reliable Mineral Phase Segmentation of 3D X-ray Computed Tomography Images

Asadi

Beckingham

2021

Energies

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X-ray CT imaging provides a 3D view of a sample and is a powerful tool for investigating the internal features of porous rock. Reliable phase segmentation in these images is highly necessary but, like any other digital rock imaging technique, is time-consuming, labor-intensive, and subjective. Combining 3D X-ray CT imaging with machine learning methods that can simultaneously consider several extracted features in addition to color attenuation, is a promising and powerful method for reliable phase segmentation. Machine learning-based phase segmentation of X-ray CT images enables faster data collection and interpretation than traditional methods. This study investigates the performance of several filtering techniques with three machine learning methods and a deep learning method to assess the potential for reliable feature extraction and pixel-level phase segmentation of X-ray CT images. Features were first extracted from images using well-known filters and from the second convolutional layer of the pre-trained VGG16 architecture. Then, K-means clustering, Random Forest, and Feed Forward Artificial Neural Network methods, as well as the modified U-Net model, were applied to the extracted input features. The models’ performances were then compared and contrasted to determine the influence of the machine learning method and input features on reliable phase segmentation. The results showed considering more dimensionality has promising results and all classification algorithms result in high accuracy ranging from 0.87 to 0.94. Feature-based Random Forest demonstrated the best performance among the machine learning models, with an accuracy of 0.88 for Mancos and 0.94 for Marcellus. The U-Net model with the linear combination of focal and dice loss also performed well with an accuracy of 0.91 and 0.93 for Mancos and Marcellus, respectively. In general, considering more features provided promising and reliable segmentation results that are valuable for analyzing the composition of dense samples, such as shales, which are significant unconventional reservoirs in oil recovery.

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Role of Mineralogy in Controlling Fracture Formation

Cited by 2 publications

References 0 publications

A Quantitative Approach to the Analysis of Reactive Mineralogy and Surface Area

A Quantitative Approach to the Analysis of Reactive Mineralogy and Surface Area

Integrating Machine/Deep Learning Methods and Filtering Techniques for Reliable Mineral Phase Segmentation of 3D X-ray Computed Tomography Images

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