Reactive Transport Modeling of Natural Gas Molecular and Isotopic Evolution During Diffusive Transport in the Subsurface
Bibliographic record
Abstract
Abstract Reactive transport modeling was employed to investigate the relative importance of fractionations associated with gas solubility, sorption and diffusive transport on dissolved methane, ethane and propane concentrations and the isotopic composition of carbon in methane (δ13C1) in groundwater. Temperature, pressure and salinity dependencies for the hydrocarbon gases were incorporated. Gas molecular ratios, C1/(C2 + C3), increased with diffusive transport, transitioning from thermogenic values to values typically indicative of biogenic gas sources, >1,000, at the leading edge of the diffusive front. Diffusive isotopic fractionation had a large effect on δ13C1 values, with fractionations ranging from −36‰ to −107‰, the difference being a function of the diffusive fractionation factor (αD0). Larger fractionations resulted from αD0 determined at relatively low pressures, 5–50 atm, and temperatures, 20°C–25°C. Less fractionation occurred with αD0 measured at higher pressures and temperature, 30–89 atm and 90°C. The extremely depleted δ13C1 values indicated from the modeling, less than −110‰, have not been observed in shallow groundwater, suggesting that diffusive fractionation of δ13C1 is offset by other processes such as microbial oxidation. 2k factorial analysis was used to assess the model sensitivity to specific parameters: estimations of hydrocarbon travel distance are most sensitive to porosity and tortuosity, while the molecular ratio was most sensitive to the free‐water diffusion coefficient, and the isotopic fractionation was most sensitive to αD0. The magnitude of diffusive fractionation on the molecular and isotopic composition of transported hydrocarbon gas may be similar to fractionations from microbial oxidation and mixing between different sources.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".