Author

Chunxiao Li

Date of Award

January 2020

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Geological Engineering

First Advisor

Dongmei Wang

Abstract

Understanding the mechanical properties of the tight formation rocks has been an area of research in the past decade due to its importance in hydraulic fracturing and rock physics modeling. Most of the tight rocks are highly heterogeneous in constituent components, a detailed understanding of mechanical properties at multiple scales is important.

In this dissertation, a combination of mechanical analytical methods including atomic force microscopy, nanoindentation/microindentation, triaxial compressive tests with computational calculations including micromechanical modeling, machine learning techniques were used. In this research, the objectives are to characterize the mechanical properties of the Bakken Formation at multiple scales and to investigate the relationship between mechanical properties measured at different scales.

AFM PeakForce nano-mechanical mapping model provides an advanced way to detect the mechanical properties of organic matter at a high resolution. Nanoindentation along with deconvolution clustering is a useful technique to investigate the mechanical properties of the Bakken Formation at a fine-scale. At the macroscale, the mechanical properties of the Middle Bakken rocks are pressure-dependent, and obvious plastic deformation was observed. The homogenization modeling can upscale the nanoscale properties to the macroscale. Convolutional neural networks trained on mineral images and microindentation moduli provided a new way to predict the mechanical properties of the Bakken Formation.

Available for download on Sunday, September 04, 2022

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