Proppants—What 30 Years of Study Have Taught Us
Bibliographic record
Abstract
Summary The earliest hydraulic-fracture stimulations used poorly sorted river sand as the proppant. Since this first experiment with proppants, the industry has evolved to offer a broad range of proppant choices although, by far, natural sand of some type remains the proppant of choice for a variety of reasons. For the past 30 years, an industry Consortium has engaged in a continuous program of building knowledge and understanding in the behavior of all types of proppants used in hydraulic fracturing. There are many basic understandings about the behavior of proppant packs under downhole reservoir conditions that have been developed through thousands of tests that have been performed through this work. These include the effects of proppant type, grain failure, fines migration, embedment, non-Darcy and multiphase flow, cycling, loading, packing arrangement, fracture-fluid damage, and others. All these effects can be at work simultaneously to negatively affect flow in the propped fracture, and the recognition of these effects assists in explaining observed well performance. This paper will present current knowledge of proppant performance that is sometimes misunderstood or wrongly applied and will assist the practicing engineer in well diagnostics and stimulation design.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".