Sandstone vs. carbonate petroleum reservoirs: A global perspective on porosity-depth and porosity-permeability relationships
Bibliographic record
Abstract
Abstract Plots are presented comparing average porosity vs. depth for 30,122 siliciclastic petroleum reservoirs and 10,481 carbonate petroleum reservoirs covering all petroleum-producing countries except Canada. However, separate plots cover 5534 siliciclastic and 2830 carbonate reservoirs of the Alberta basin in Canada. Average permeability vs. average porosity is shown for the non-Canadian reservoirs. Key similarities and differences between sandstones and carbonates are noted, and implications are discussed regarding the dominant factors controlling reservoir quality in each lithology. Trends of steadily decreasing median and maximum porosity with increasing depth reflect burial diagenetic porosity loss in response to increasing thermal exposure with depth. These trends seem inconsistent with the suggestions that both sandstones and carbonates commonly increase in porosity by dissolution during deeper burial. Carbonate reservoirs have lower values of median and maximum porosity for a given burial depth, probably because of greater chemical reactivity of carbonate minerals relative to quartz and the resulting lower resistance to chemical compaction and associated cementation. Relative paucity of low-porosity (0–8%) siliciclastic reservoirs at all depths compared with carbonates may reflect the more common occurrence of fractures in carbonates and the effectiveness of these fractures for facilitating economic flow rates in low-porosity rock. Overall, carbonate reservoirs do not have lower permeability for a given porosity compared with sandstones but do have strikingly lower proportions of both high-porosity and high-permeability values. The data presented can serve as a general guide for the distribution of reservoir quality that can reasonably be expected in exploration wells drilled to any given depth in the absence of detailed geologic information, such as burial and thermal history.
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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 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".