University of Regina

Vibration-assisted water flooding (VA-WF) can improve sweep efficiency. However, unclear macro-scale mechanisms limit its wider adoption in heavy oil reservoirs. This study combines previous sandpack experiments with two-dimensional Volume-of-Fluid (VOF) simulations to show how vibrations reshape permeability fields and, in turn, pressure and production behaviour. Heavy oil sandpacks were water-flooded under conditions of no vibration and 2 Hz and 5 Hz axial excitation. Measured injection pressure histories and oil production were used to calibrate a VOF model in which absolute permeability follows a log-normal distribution with directional anisotropy. Only when axial and radial permeabilities were assigned a negative local correlation did the model reproduce key observations: secondary pressure spikes, irregular viscous-fingering morphologies, delayed production drops, and variability in cumulative recovery. Parameter sweeps quantify the sensitivity of VA-WF performance to the variance and correlation of the permeability field, and multiple runs estimate the variability in outcomes introduced by stochastic heterogeneity. This study proposes a transferable workflow—comprising sample testing, parameter inference, and probabilistic simulation—to screen excitation conditions and forecast VA-WF performance prior to field implementation, enabling operators to optimize vibration frequency based on reservoir-specific permeability characteristics and to anticipate production variability under uncertainty. These results highlight the dominant factors affecting swept volume and oil recovery, supporting data-driven decision making in VA-WF projects.

When undertaking complex search tasks that cannot be completed in a single session, resuming the search task can be challenging. When the search is conducted over multiple platforms, this challenge increases. While aggregated search interfaces support the synthesis of search results from multiple sources, cross-session support in such settings remains underexplored. In this research, we study a set of novel workspace approaches to this problem, within a digital humanities search context. We introduce an interactive cluster refinement (ICR) workspace that automatically organizes saved resources using thematic clustering, preserves their provenance, and provides an interactive method for refining the cluster to match the searcher’s mental model. Building on this foundation, we investigate two augmentations to facilitate reacquaintance with previous search activities: interactive passage highlighting (ICR+IPH), which lets searchers mark salient passages as a reminder of why resources were saved; and AI-generated summarization (ICR+AIS), which produces concise overviews of saved resources with inline citations. We combine these together into a fourth interface (ICR+IPH+AIS), where the AI-generated summarization is instructed to make use of the passage highlighting. We conducted a cross-session laboratory study with n = 36 participants to compare these interface alternatives with respect to measures of utility, perceived value, metacognition, exploratory search behaviour, and cross-session support. Both ICR+IPH and ICR+AIS improved outcomes relative to ICR. The combined ICR+IPH+AIS interface was most effective in enabling cross-session searching, yielding faster task resumption, higher resource precision, greater perceived value and knowledge gain, and stronger support for metacognitive planning and evaluation as well as exploratory search outcomes.

Abstract The empirical association between Cr-bearing green micas (i.e., muscovite var. fuchsite and mariposite) and gold deposits has been known for more than a century, but details regarding the genetic relationship between the two remain unknown. Using the 513 t Au Kerr-Addison deposit (Virginiatown, Ontario, Canada) as a case study, this contribution seeks to (1) characterize and model the progressive hydration and carbonation of komatiites using petrography and thermodynamic modeling; (2) investigate the distal to proximal variations in mineral chemistry of spinel-, chlorite-, and dioctahedral mica-group minerals using scanning electron microscopy–energy dispersive spectroscopy and shortwave infrared reflectance spectroscopy; and (3) evaluate how Cr is mobilized from precursor minerals and ultimately incorporated into muscovite using modal analysis and scanning electron microscopy–energy dispersive spectroscopy. Results show: The observed alteration assemblages of komatiitic protoliths (i.e., hydrated komatiite, regional carbonation, talc-chlorite, carbonate-chlorite, and carbonate-muscovite facies), as well as observed reaction textures (tremolite → dolomite, talc → magnesite + quartz, and chlorite → muscovite) are compatible with thermodynamic modeling of progressively carbonated komatiites in the presence of K+ (in the Na2O–CaO–K2O–MgO–Al2O3–SiO2–H2O–CO2, i.e., NCKMASHC, composition space). Komatiitic protoliths exhibit different initial alteration assemblages compared to harzburgitic protoliths because komatiites have higher bulk-rock SiO2 contents than harzburgites. This highlights the importance of host-rock control on observed alteration mineral assemblages, as well as mechanisms for gold deposition (e.g., sulfidation versus dolomitization).Exploration implications for mineral chemistry. All chlorite-group minerals are clinochlore in composition and display a distal to proximal zonation of increasing Al content via Tschermak substitution. This zonation correlates with an increasing shift of the ∼2250 nm peak position in shortwave infrared reflectance spectra. The trend is consistent with the progressive carbonation of clinochlore under increasing log fCO2 conditions. Muscovite shows an inconsequential increase in Al content toward proximal zones, a feature which may be advantageous for global-scale comparisons between different deposits and environments.Source and pathway for chromium. Two sources of Cr are present: silicates (olivine and clinopyroxene, ∼55%) and spinel-group minerals (magnesiochromite to chromite, ∼45%). Both break down early in the alteration sequence (within the hydration, regional carbonation, and talc-chlorite facies), releasing Cr to form clinochlore. With increasing log fCO2, clinochlore is replaced by muscovite, which inherits its Cr contents. Results indicate that (1) muscovite var. fuchsite is dominantly derived from the carbonation of Cr-bearing clinochlore under high log fCO2conditions (>2.3–2.7 log units) in the presence of K+; (2) Al content in clinochlore can be used as a proxy for fluid–rock reactions, and the trend may potentially be mapped on the deposit scale using shortwave infrared reflectance spectroscopy; and (3) Cr is mineralogically mobile, being sourced from primary silicates and oxides, retained in clinochlore, and finally inherited in muscovite in proximal settings.

Atmospheric moisture availability plays a central role in regulating the occurrence and persistence of extreme precipitation, but its temporal linkage with large-scale moisture transport remains insufficiently quantified at the global scale. This study examines the lagged relationship between moisture transport across the land–ocean interface and extreme precipitation using ERA5 reanalysis data for the period 1979–2020. Extreme precipitation is characterized using the R95pTOT index, which measures the total precipitation accumulated during very wet periods. Vertically integrated moisture fluxes are projected onto coastal boundaries to quantify inflow, outflow, and netflow components of moisture transport. Lagged Pearson correlations as well as the p-value between these components and R95pTOT are evaluated globally and for four representative regions: the Asian Monsoon, the Amazon Basin, the Gulf of Mexico and North America, and West Africa. The results show that moisture inflow is positively associated with extreme precipitation across most regions, indicating that enhanced ocean-to-land moisture transport supports increased atmospheric moisture availability during extreme events. The strongest and most persistent relationships are found in tropical regions, where significant inflow–precipitation correlations persist for approximately 5–10 days. In contrast, mid-latitude coastal regions exhibit weaker and more transient relationships, consistent with the influence of rapidly evolving synoptic systems. Netflow correlations generally display weaker and more regionally dependent associations with extreme precipitation, with outflow showing weak or negative relationships in some regions, particularly in West Africa. Overall, the findings demonstrate that both the direction and temporal persistence of moisture transport play an important role in shaping regional differences in extreme precipitation.

Oilseed production in the Canadian Prairies is highly sensitive to interacting climatic, soil, and landscape constraints, which has important implications for long-term agricultural sustainability under climate change. Canola and flax are economically significant Prairie oilseeds with distinct sensitivities to temperature and moisture variability; however, region-wide, crop-specific suitability assessments remain limited. In this study, we developed a machine-learning-enhanced Land Suitability Rating System (LSRS) framework to evaluate both historical and projected suitability for canola and flax across the Prairie agricultural region. Random Forest regression models were trained using spatial crop-density data in combination with observed climate normals and updated soil and terrain variables. Variable-importance scores were used to derive empirical climate–soil weighting, resulting in climate contributions of 70% for canola and 75% for flax. Model performance demonstrated strong internal agreement with observed spatial patterns (R2 ≈ 0.99). Canola suitability was primarily associated with mid- to late-season precipitation and maximum temperatures, whereas flax suitability was more strongly influenced by growing-season minimum temperatures. Future suitability was simulated using CanDCS-M6 downscaled CMIP6 projections under SSP2-4.5, SSP3-7.0, and SSP5-8.5. Projected warming and regionally variable precipitation changes produced spatially heterogeneous shifts in suitability, including localized gains in northern areas and increased climatic risk across parts of the southern Prairies under higher-emission scenarios. The proposed framework integrates empirical machine-learning insights with an interpretable suitability rating system, providing a scalable and policy-relevant tool to support climate-informed adaptation and sustainable oilseed production planning.