Quantifying the Role of Hematite Cement in Controlling Permeability in a Deep Tight Gas Reservoir from the North Sea
Bibliographic record
Abstract
Abstract Recent work has shown the potential usefulness of magnetic susceptibility and hysteresis techniques in assessing the impact of fine grained hematite on permeability in red and white sandstone samples (Potter et al. 2009; Ali and Potter, 2011b). The present study demonstrates that hematite cementation is a major controlling factor on permeability in a deep tight gas reservoir in the North Sea. Magnetic susceptibility measurements undertaken on core plugs in this reservoir showed a strong correlation with probe permeability performed on the same plug samples. Moreover, samples with a higher content of hematite exhibited lower permeability values. Thin section analysis revealed the presence of a thin (approximately 10-15µm) rim of hematite cement surrounding quartz grains, which block pore connections and reduce permeability. Magnetic hysteresis measurements on some representative samples indicated a very similar paramagnetic clay content in both the low and high permeability samples suggesting that the clay (mainly illite) is not the dominant controlling factor producing the variations in permeability that we observed. Since samples with higher hematite content exhibit lower permeability it appears that hematite is a major control on the permeability variations seen in this reservoir. Whilst the paramagnetic clays undoubtedly have an influence on the absolute permeability value, since increasing paramagnetic clay content has previously been shown to correlated with decreasing permeability (Potter, 2007), small amounts of hematite cement can significantly further reduce the permeability. Analysis of the magnetic hysteresis parameters on a Day plot indicated that the permeability was essentially independent of the hematite particle size for the fine particle sizes observed in this study.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".