Bibliographic record
Abstract
Naturally fractured carbonate reservoirs (NFCRs) comprise the majority of the oil and gas reservoirs around the Persian Gulf. Many of these reservoirs have a long history of exploitation, but vast amounts of oil remain in place. A major redevelopment process for light oil based NFRs will likely be the use of horizontal wells combined with gravity drainage at constant pressure based on voidage replacement with natural gas (top-down) and natural bottom water drive or deliberate bottom water injection (bottom up), or controlled flank water invasion for reservoirs with adequate dip. The excellent recovery factors achieved in Alberta NFCRs depends on appropriate well placement, careful voidage replacement management, and continuous monitoring of pressures, rates and fluid ratios. Geomechanical aspects of such a redevelopment approach may involve the placing of horizontal wells in orientations conducive to small-scale well stimulation activities revolving around hydraulic fracturing. Such fracturing helps guarantee that sufficient aperture vertical channels are available so that stable gravity drainage can develop and give adequate production rates per well. The proposed approach and information needs for the proper placement of wells and appropriate stimulation practices are outlined. In particular, good understanding of reservoir permeability distribution, water/oil interfaces, lithology data, and in situ stress field data are needed, and this is more challenging in reservoirs that have already gone through some amount of pressure depletion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".