What Can Groundwater Monitoring Tell Us About Gas Migration? A Numerical Modeling Study
Bibliographic record
Abstract
Abstract Groundwater monitoring to measure a variety of indicator parameters including dissolved gas concentrations, total dissolved gas pressure (TDGP), and redox indicators is commonly used to evaluate the impacts of gas migration (GM) from energy development in shallow aquifer systems. However, these parameters can be challenging to interpret due to complex free‐phase gas source architecture, multicomponent partitioning, and biogeochemical reactions. A series of numerical simulations using a gas flow model and a reactive transport model were conducted to delineate the anticipated evolution of indicator parameters following GM in an aquifer under a variety of physical and biogeochemical conditions. The simulations illustrate how multicomponent mass transfer processes and biogeochemical reactions create unexpected spatial and temporal variations in several analytes. The results indicate that care must be taken when interpreting measured indicator parameters including dissolved hydrocarbon concentrations and TDGP, as the presence of dissolved gases in background groundwater and biogeochemical processes can cause potentially misleading conclusions about the impact of GM. Based on the consideration of multicomponent gas partitioning in this study, it is suggested that dissolved background gases such as N 2 and Ar can provide valuable insights on the presence, longevity and fate of free‐phase natural gas in aquifer systems. Overall, these results contribute to developing a better understanding of indicators for GM in groundwater, which will aid the planning of future monitoring networks and subsequent data interpretation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".