Shale gas condensate reservoirs, characterized by high organic matter content and ultra-low permeability, exhibit complex fluid transport behavior governed by adsorption-desorption and molecular diffusion. Although CO2 huff-n-puff injection has shown promise for alleviating condensate blockage, enhancing hydrocarbon recovery, and enabling CO2 storage, the coupled adsorption-diffusion mechanisms remain poorly understood. In this study, a compositional numerical model was developed by coupling adsorption-diffusion processes with multiple CO2 trapping mechanisms to simulate the huff-n-puff process. The model was used to evaluate the effects of injection parameters, permeability, and total organic carbon (TOC) content on condensate mitigation, hydrocarbon recovery, and CO2 storage performance. Simulation results indicate that molecular diffusion promotes condensate re-vaporization, resulting in a modest increase in gas production but a slight decrease in oil recovery, with its impact diminishing as permeability decreases. Reservoirs with higher TOC exhibit improved methane recovery and greater CO2 sequestration potential due to competitive adsorption between CO2 and CH4. The overall CO2 storage efficiency ranges from 48% to 66%, with adsorption trapping identified as the dominant mechanism. These results highlight the critical role of adsorption in governing both hydrocarbon recovery and CO2 retention. The developed modeling framework provides new insights into optimizing CO2 huff-n-puff operations in shale gas condensate reservoirs, supporting the dual objectives of enhanced recovery and geological carbon storage.
Methane mitigation is often evaluated by sector or by reactor performance, although deployment changes energy, food, waste, and wastewater systems beyond the point of treatment. This critical review evaluates prevention, recovery, destruction, and atmospheric removal within a common life-cycle framework. Evidence is screened by methane concentration, source controllability, recovery value, auxiliary demand, burden shifting, evidence maturity, governance fit, and net cost and incentives. Across sectors, prevention is considered first when methane generation or release can be avoided before dilution. Recovery is credible for concentrated streams only when collection losses are low and recovered products displace existing supply. Destruction is justified for controlled residual streams, but conversion efficiency alone does not establish net benefit. Atmospheric methane removal remains an early stage option. At a concentration of approximately 1.9 ppm, methane removal requires large volumes of air and may impose substantial energy, land, and material demands. Reported technology rankings are conditional on the counterfactual, system boundary, operating context, and evidence scale. The resulting stage-gated framework links production and consumption choices to intervention timing, life-cycle performance, and verification. Atmospheric removal is treated as a research contingency, not a substitute for source mitigation. The final gates specify the evidence required before an emerging approach can progress beyond controlled research.
Abstract This study evaluates concrete in which natural aggregates (NAs) are replaced by electric arc furnace (EAF) slag siderurgical aggregate (SA), with partial cement replacement by silica fume and reinforcement using steel and polypropylene fibers. Mechanical tests (compressive and flexural) at 7, 28, and 90 days and microstructural analyses (SEM/EDS) of the interfacial transition zone (ITZ) were conducted. Optimal mixes containing 8% silica fume and 0.6% fiber volume exhibited an average ITZ thickness reduction from 27.8 ± 3.2 μ m (control) to 15.4 ± 2.1 μ m ( n = 20 fields), and compressive strength gains of up to 21% at 28 days (and up to 30% at 90 days for SA-C-100 mixes). Statistical significance was assessed using one-way ANOVA. These results demonstrate the synergistic effect of SA, silica fume, and fibers on densifying the ITZ and improving flexural toughness, and provide practical recommendations for mass concrete applications.
Accurately estimating crop yields across scales (e.g., regional administrative and field scales) is critical for global food security, given mounting challenges agriculture faces from climate change, population growth, soil degradation, and water scarcity. Remote sensing has been widely used for crop yield estimation with its spatially extensive coverage and temporally continuous observations of crop conditions. However, robust yield estimation across scales remains challenging, particularly under conditions of limited availability of crop yield and crop management data. While process-based crop models can capture complex interactions of plant-soil-atmosphere continuum, their large-scale application is limited by computational cost, parameterization difficulty, and model input (e.g., management) constraints. Empirical data-driven models are more flexible and computationally efficient, but they heavily depend on ground-measured yield labels that are often scarce over space and time. To address these challenges, we propose PHYIELD, a novel hybrid yield estimation framework that integrates systematically calibrated process-based crop modeling and phenology-guided deep learning for maize yield estimation at both county and field scales under yield data constraint conditions. PHYIELD mainly comprises three components: systematic crop model calibration, diversified crop model simulation, and phenology-guided deep learning. Given the challenges and prevalence of sparse yield observations in practice, PHYIELD is uniquely devised with limited ground yield records for model building. Its model performance is systematically evaluated for temporal, spatial, and scale transfers, upon comparison with standalone crop model and deep learning counterpart benchmarks. Results showed that the systematic crop model calibration of PHYIELD, particularly via the calibration for Leaf Area Index (LAI), followed by yield (biomass multiplied by Harvest Index [HI]), is essential for region-varying parameterizations of the crop model before coupling with deep learning. With diversified simulations from calibrated crop model and phenology-guided deep learning, PHYIELD achieved correlation (R) of 0.53–0.73, bias of −1.47–0.96 t ha⁻1, and root mean square error (RMSE) of 1.07–1.68 t ha⁻1 in the study region, outperforming both crop model and deep learning benchmarks. Under significant yield data constraint condition (e.g., only 2023 county-level yield data of Illinois available), PHYIELD demonstrated superior temporal transferability over the preceding 20 year period (2003–2022) (R = 0.70, RMSE = 1.76 t ha⁻1), spatial transfer to surrounding states (R = 0.85, RMSE = 1.06 t ha⁻1), and scale transfer from county to field levels (R = 0.53–0.76, RMSE = 1.55–1.58 t ha⁻1). By leveraging the complementary strengths of process-based and data-driven modeling approaches, coupled with systematic parameterization of crop model and incorporation of phenology knowledge, PHYIELD provides a more robust and generalizable solution for multi-scale maize yield estimation, particularly under limited data conditions. It can largely support yield-oriented decision-making in agricultural management, policy development, and food security planning.
