Quantifying past subglacial methane storage and emissions under the Fennoscandian ice sheet by means of historic data from hydrocarbon industry
Bibliographic record
Abstract
Large-scale methane releases from geological hydrocarbon seepage and the dissociation of subseafloor gas hydrates under shallow waters are important drivers of atmospheric methane concentration increases and global warming. However, the processes, timescales, and fluxes involved in such emissions remain insufficiently constrained. In this study, we redeploy historic oil and gas industry datasets—including well logs, seismic data, as well as reservoir temperature and pressure data—to reveal reservoir-scale methane leakage, storage and release dynamics in the Håkjerringdjupet area at the offshore continental margin of Norway beneath the past Fennoscandian ice sheet during the last glacial maximum.Our numerical approach considers glacial loading causing the overpressurization of a shallow gas reservoir, driving the expulsion of methane-rich fluids through faulted zones and into subglacial sediments. Glacially-driven pressure increments led to extensive methane hydrate formation within these sediments, storing carbon and significantly improving basal traction. Laboratory shear-strength measurements (Spangenberg et al., 2020), integrated with subglacial hydrate formation modelling (Li et al., 2022, 2023), indicate a minimum hydrate saturation to regulate glacial flow, with the subglacial hydrate system storing ~0.48 Gt of methane in Håkjerringdjupet. During deglaciation, we estimate that ~120–240 Tg of methane released by hydrate dissociation may have reached the atmosphere shortly after the last glacier retreated (about 16,000 years before the present).Our findings highlight how legacy industry well data and conventional oil and gas technologies can be harnessed to advance understanding of subglacial carbon storage and fluid migration in response to climate change. Our work provides an insightful Pleistocene analogue for studying contemporary ice-sheet-driven methane storage and release, informing strategies for sustainable carbon management in the transition towards net zero emissions.References:Li, Z., Spangenberg, E., Schicks, J. M., and Kempka, T.: Numerical Simulation of Coastal Sub-Permafrost Gas Hydrate Formation in the Mackenzie Delta, Canadian Arctic, Energies, 15, 4986, https://doi.org/10.3390/en15144986, 2022.Li, Z., Chabab, E., Spangenberg, E., Schicks, J. M., and Kempka, T.: Geologic controls on the genesis of the Arctic permafrost and sub-permafrost methane hydrate-bearing system in the Beaufort–Mackenzie Delta, Front. Earth Sci., 11, 1148765, https://doi.org/10.3389/feart.2023.1148765, 2023.Spangenberg, E., Heeschen, K. U., Giese, R., and Schicks, J. M.: “Ester”—A new ring-shear-apparatus for hydrate-bearing sediments, Review of Scientific Instruments, 91, 064503, https://doi.org/10.1063/1.5138696, 2020.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".