Microbial Signatures in Oil Reservoirs: Biomarker Stability and Their Role in Subsurface Fluid Monitoring
Bibliographic record
Abstract
Sustainable energy solutions such as carbon capture, utilization, and storage (CCUS), geothermal energy, and hydrogen storage are vital for achieving low-carbon energy goals. However, these technologies face significant challenges, including gas leakage and the need for reliable monitoring systems within subsurface geological formations. Indigenous microorganisms, naturally widespread in these formations, offer a novel approach for dynamic monitoring over time through DNA sequencing analysis. Yet, critical questions remain: Can ground-level samples accurately represent geological information? How stable is the microbial DNA under surface conditions and for how long? This study investigates the stability of microbial community structures from deep subsurface oil reservoir samples during degradation at room temperature over 120 h. Samples were analyzed at 24 h intervals using DNA extraction, concentration measurements, and sequencing. Microbial diversity was assessed via α and β diversity indexes, while Venn analysis compared community structures to identify formation-specific genera. Findings reveal that microbial profiles from subsurface samples remain largely reflective of their original environments despite surface degradation. This highlights the feasibility of using subsurface microbial biosensing for dynamic environmental monitoring. By addressing the stability of microbial data, this research enhances the potential of DNA-based tools to support CCUS, geothermal energy, and hydrogen storage. It underscores the role of microbial biosensing in advancing sustainable energy practices, offering a robust framework for tackling challenges in subsurface monitoring, while contributing to the transition toward a low-carbon future.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".