The injection of CO2 into subsurface reservoirs provides a long term solution for anthropogenic emissions. A variable injection rate (such as ramping the flow rate up or down) provides flexibility to injection sites, and could influence the amount of residual trapping. Observations made in cm-scale samples showed that starting at a low flow rate established a flow pathway across the core, leading to a long term reduction in pore space utilization, as increases in flux were accommodated with little change in saturation. In this work we assess the scalability of these observations by performing experiments with variable injection rates in larger samples: 5 cm diameter and 12 cm length, compared to 2.5 cm diameter and 4.5 cm length in our previous work (Spurin et al., 2024). We observed that starting at a low flow rate did not lead to a long term reduction in pore space utilization. Instead saturation increased significantly with increased flux, leading to a higher pore space utilization than experiments where injection started with the higher flow rate. The difference in observations depending on sample size and the role of heterogeneity highlights potential uncertainties in upscaling experimental observations to field-scale applications.

The transport of chemical species in rocks is affected by their structural heterogeneity to yield a wide spectrum of local solute concentrations. To quantify such imperfect mixing, advanced methodologies are needed that augment the traditional breakthrough curve analysis by probing solute concentration within the fluids locally. Here, we demonstrate the application of asynchronous, multimodality imaging by X-ray computed tomography (XCT) and positron emission tomography (PET) to the study of passive tracer experiments in laboratory rock cores. The four-dimensional concentration maps measured by PET reveal specific signatures of the transport process, which we have quantified using fundamental measures of mixing and spreading. We observe that the extent of solute spreading correlate strongly with the strength of subcore-scale porosity heterogeneity measured by XCT, while dilution is enhanced in rocks containing substantial sub-micron porosity. We observe that the analysis of different metrics is necessary, as they can differ in their sensitivity to the strength and forms of heterogeneity. The multimodality imaging approach is uniquely suited to probe the fundamental difference between spreading and mixing in heterogeneous media. We propose that when multi-dimensional data is available, mixing and spreading can be independently quantified using the same metric. We also demonstrate that one-dimensional transport models have limited predictive ability towards the internal evolution of the solute concentration, when the model is solely calibrated against the effluent breakthrough curves. The dataset generated in this study can be used to build realistic digital rock models and to benchmark transport simulations that account deterministically for rock property heterogeneity.

Quantification of heterogeneous multiscale permeability in geologic porous media is key for understanding and predicting flow and transport processes in the subsurface. Recent utilization of in situ imaging, specifically positron emission tomography (PET), enables the measurement of three-dimensional (3-D) time-lapse radiotracer solute transport in geologic media. However, accurate and computationally efficient characterization of the permeability distribution that controls the solute transport process remains challenging. Leveraging the relationship between local permeability variation and solute advection rates, an encoder-decoder based convolutional neural network (CNN) is implemented as a permeability inversion scheme using a single PET scan of a radiotracer pulse injection experiment as input. The CNN consists of Densely Connected Neural Networks that can accurately capture the 3-D spatial correlation between the permeability and the radiotracer solute arrival time difference maps in geologic cores. We first test the inversion accuracy using 500 synthetic test datasets. We then use a suite of experimental PET imaging datasets acquired on four different geologic cores. The network-inverted permeability maps from the geologic cores are used to parameterize forward numerical models that are directly compared with the experimental PET imaging datasets. The results indicate that a single trained network can generate robust, denoised 3-D permeability inversion maps in seconds. Numerical models parameterized with these permeability maps closely capture the experimental solute arrival time behavior. This approach presents an unprecedented improvement for efficiently characterizing multiscale permeability heterogeneity in complex geologic materials.