Exceptional Proppant Flowback Control for the Most Extreme Well Environments: The Shape of Things to Come
Bibliographic record
Abstract
Abstract Proppant flowback within deep hot wells and/or highly productive wells is a major problem in the oil and gas industry. Under these extreme conditions, many of the current products and processes to control flowback often fail. As such, improved or alternative technology and procedures are constantly being sought. One such technology is the development of deformable proppants. Material and structural improvement to a deformable proppant has allowed laboratory test conditions to be extended to higher temperature, closure stress and flowrate. As a result of this fine tuning, exceptional proppant flowback control has been obtained. Testing of this new deformable proppant, blended with typical fracturing proppant, has shown 50 fold increases in flowrate and 100 fold increases in pressure drop are attainable without pack failure, while still maintaining fracture conductivity. Furthermore, this deformable proppant has been applied in wells where current technology would either fail or have serious drawbacks. In two primarily gas producing reservoirs, the addition of this deformable proppant to proppant packs placed during fracturing treatments, has been observed to very effectively control proppant flowback under conditions of high bottom hole temperature, high fracture closure stress and high production regimes. In order to facilitate field application, new addition and monitoring procedures were developed to accomplish these successful fracturing operations. The developmental testing, successful application and well performance all indicates significant improvement in proppant pack integrity.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".