Bibliographic record
Abstract
Summary Diatoms and radiolarians are microorganisms that precipitate Opal-A to form siliceous tests that accumulate on the seafloor to form siliceous oozes. Progressive diagenesis of these deposits during burial results in thick, highly compressible reservoirs of exceptionally high porosity and low permeability, not unlike the chalk reservoirs of the North Sea. During burial and over time, the amorphous silica phase (Opal-A) becomes unstable and gradually changes in its structure to more stable, ordered Opal-A′ and crystalline forms or phases of silica, namely Opal-CT and quartz. The Opal-A→Opal-A′→Opal-CT→quartz transformation results in a naturally occurring densification and compaction process that is accelerated by an application of heat. Reservoir compaction and surface subsidence can usually be controlled by injecting fluid to control the effective stress. However, in heavy-oil diatomite reservoirs undergoing steam injection, the injected fluid causes competing effects: it controls effective stress to some degree, yet at the same time it accelerates compaction and subsidence. This paper describes selected results of a diatomite laboratory testing program and features of a unique thermal reservoir simulator formulated to handle the effects on compaction caused by stress, temperature, and time-dependent strain (creep). Elevated temperature in amorphous Opal-A diatomite is shown to be capable of causing a sample compression of 25% or more and a severe reduction in permeability. The effects of thermally induced compaction are expected to accelerate surface subsidence as diatomite steam projects mature.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".