Geological engineering criteria for deep solids injection
Bibliographic record
Abstract
Abstract Slurried solids injection is a procedure for placement of granular solid waste deep into porous and permeable geological strata. This technology is currently used by the petroleum industry in several countries (United States, Canada, North Sea, and Indonesia) to dispose of nonhazardous oil-field waste solids. Granular or ground solids of grain size less than 5 mm (0.19 in.) are mixed with waste liquids to form a slurry that is pumped down a deep well under conditions of continuous hydraulic fracturing pressure (pinj > σv). This article discusses rock mechanics aspects and geological engineering criteria for deep slurry injection. Specifically, the geometrical, lithostratigraphical, and physical parameters that characterize a stratum as a suitable target reservoir for slurried waste placement are addressed. The most important criteria are permeability, porosity, reservoir thickness, depth, and structural geology (of the region). A geological assessment model was developed to serve as a screening process to select suitable reservoirs for slurried waste placement. The screening process to select suitable reservoirs is composed of two steps: a decision tree and a semiquantitative ranking system that provides a numerical score for the stratum. The apparently robust assessment model was tested on several sites representing diverse sedimentary geology in the United States, Canada, the North Sea, and Indonesia.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".