Bibliographic record
Abstract
Abstract Diatomite oil reservoirs are unconventional. They hold billions of barrels of oil in a tight rock matrix with unusual physical properties, and contain a stress-sensitive natural fracture system that introduces a strong permeability anisotropy during fluid injection. Perhaps the most unusual feature of diatomite is geochemical in nature. Diatomite undergoes a silica-phase reordering and transformation as temperature is raised, whereby amorphous Opal-A is converted to a more ordered Opal-A' and more dense, crystalline Opal-CT. The injection of steam accelerates this naturally occurring process and leads to rapid densification and compaction and an irreversible loss of permeability. Nearly all of the field projects have been installed in Opal-A. Opal-CT is less permeable and this is the primary reason its development has been deferred. Although Opal-CT is less permeable under initial reservoir conditions, its stress and temperature-dependent compaction coefficients are much lower than those of Opal-A. As steam injection elevates reservoir temperature over time, the difference in permeability of Opal-A and Opal-CT is expected to narrow. It is chiefly for this reason that Opal-CT may hold more promise than currently supposed. The Opal-A field projects have demonstrated that closely spaced rows of wells are necessary for efficient oil recovery by water or steam injection. What is now called ultra-tight well spacing with rows of hydraulically fractured injectors and producers spaced only 35 to 45 feet apart was shown via numerical modeling more than 20 years ago to be necessary for maximizing oil recovery. That early modeling made use of laboratory determined rock and fluid properties and included the effects of thermally induced compaction. Following tuning of the early models using hydraulically fractured cyclic steam response, they were used to estimate ultimate recovery and thermal efficiency for steam injection into diatomite containing heavy oil. Given Opal-CT and ultra-tight well spacing, a combined cyclic steam and steamflood process was shown to be capable of ultimately recovering about 40% of the original oil-in-place, with a CSOR of 3 to 4 barrels of CWE steam per barrel of oil.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 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 teacher head, 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".