Multiple microorganism influences and interactions limit the reliability of underground hydrogen and carbon storage simulation models at the reservoir scale
Bibliographic record
Abstract
The various types of microorganisms encountered in subsurface reservoirs are identified, and their consequences for underground hydrogen storage and carbon dioxide storage are described. In underground hydrogen storage reservoirs, most microorganism activities have negative consequences (pore clogging, reduction in permeability, corrosion, change in composition and loss of stored gas). In underground carbon dioxide storage most of those negative consequences also apply, but transforming some of the stored CO2 into methane by the biomethanation process can be beneficial and potentially exploited. Although laboratory studies and simplified-system simulations have qualitatively explained the microorganism processes involved at the pore scale in underground hydrogen storage and underground carbon dioxide storage reservoirs, it is difficult to model these processes quantitatively at the reservoir scale. The simplifying assumptions, scales and dimensions of the majority of bioreactive transport models fail to take adequate account of reservoir heterogeneities, biofilm development complexities, periodic fluctuations in fluid-flow and nutrient supply. These limitations mean that most of the existing bioreactive transport models are unable to reliably quantify changes in gas composition, gas loss, permeability, or the degree of corrosion likely to occur across heterogeneous underground hydrogen storage or underground carbon dioxide storage reservoirs. However, several opportunities exist to improve field-scale bioreactive transport model performance for underground hydrogen storage and underground carbon dioxide storage in the coming years. These include building on the knowledge gained from the existing simplified models by incorporating new modelling techniques and more detailed reservoir scale information. Exploiting DNA sequencing offers the capability to better characterize the properties of reservoir microorganism communities. Physics-informed machine learning techniques could be tailored to provide efficient surrogate models for simulations of heterogeneous reservoirs. Such improvements should lead to simulation models capable of accommodating more complex reservoir-scale assumptions. Document Type: Review Cited as: Wood, D. Multiple microorganism influences and interactions limit the reliability of underground hydrogen and carbon storage simulation models at the reservoir scale. Sustainable Earth Resources Communications, 2025, 1(2): 53-68. https://doi.org/10.46690/serc.2025.02.04
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".