Enhancing Predictions of Carbonate Dissolution Behavior Using Quantitative Heterogeneity Metrics from XCT Data

Abstract

Carbonate rock formations are common targets for sustainable subsurface energy development, but their chemical reactivity with injection fluid creates complex dissolution pathways that are difficult to predict. Improving predictions of the physical changes that occur in formations due to chemical dissolution is essential for assessing the long-term viability of such projects. X-ray computed tomography (XCT) imagery is commonly used to study rock core samples because it allows for nondestructive 3D visualization of the pore space. Pore network heterogeneities observed from XCT are expected to affect larger-scale reactive transport behavior, but most of the work in this space to date has been done through numerical simulations. Few studies have tested the impact of these heterogeneities in laboratory studies of natural rock samples. In this dissertation, quantitative metrics of heterogeneity were developed from XCT images of limestone samples and tested for their ability to predict various aspects of dissolution. A first step in analyzing XCT data is segmentation of the dataset into pore and mineral space. This step is prone to user subjectivity and has substantial impact on subsequent interpretations of the data. Various combinations of three image processing filters were tested on XCT datasets prior to segmentation, and the use of all three filters in sequence resulted in more consistent porosity estimates that were significantly closer to experimental values. Porosity is a primary characteristic used to predict downstream petrophysical parameters, including permeability, so accurate assessment of porosity is paramount. This filtering process was used in all subsequent studies. Next, a series of core flooding experiments were performed on three limestones with different diagenetic properties. Cores were XCT scanned before and after dissolution by dilute acid in a high-pressure flowthrough apparatus. Persistent homology was applied to analyze the topology of the pore space in three limestones and observe changes in pore size, connectivity, and spatial distribution. It was observed that permeability increase was driven by the growth of large, connected pore bodies. In the core with the highest degree of along-core heterogeneity prior to reaction, pore sizes became more homogenous due to dissolution. Pore growth was particularly pronounced in pores that were locally large but not the largest in the core. Being able to predict regions with high or low pore size increase could help improve the efficiency of models of larger-scale behavior. Next, fractal dimension was used to investigate the pore space complexity of the limestone samples. Spectral analysis was used to study how the relevant spatial scales of heterogeneity evolved due to dissolution. Smaller heterogeneities lessened in importance, and larger heterogeneities contributed more to overall variance after reaction, suggesting that a larger scale of observation with lower resolution would be preferable after dissolution. The preferential flowpath that developed in each core was isolated; its fractal dimension showed good positive correlation with that of the initial pore space, suggesting that rocks with geometrically complex pore space are likely to experience more complex branching behavior. Optimizing spatial resolution and scale is essential for improving simulations that upscale the phenomena observed in the laboratory. This dissertation contributes to a growing body of work in characterizing carbonate core samples and using those characterizations to predict reactive transport behavior during dissolution. Improving predictions of dissolution behavior is essential for the implementation of sustainable subsurface energy technologies.