Sand on Demand: A Laboratory Investigation on Improving Productivity in Horizontal Wells Under Heavy-Oil Primary Production
Bibliographic record
Abstract
Summary The cold-production recovery process, also known as cold heavy-oil production with sand (CHOPS), is a method for enhancing primary heavy-oil production by aggressively producing sand (massively and persistently). It is a successful commercial recovery process in western Canada using vertical (or slanted or deviated) wells. Applications of cold-production technology with aggressive sand production in horizontal wells, however, have not met with commercial success. This paper presents the results of experiments performed to assess the feasibility of applying cold heavy-oil production with horizontal wells using less-aggressive (i.e., controlled) sand-production strategies. Specifically, the effects of slot size, confining stress, fluid velocity, and sand-grain sorting on sand production have been investigated. Preliminary results indicate that slot-size selection is critical for establishing "sand on demand." For proper slot-size selection, it is essential to know the grain-size distribution of the sand—in particular, attributes such as the size of the coarsest fraction of the sand and the sorting (uniformity coefficient) of the sand. For example, it was observed in the sand-production experiments that the critical pressure gradient for maintaining continuous sand production is much lower for well-sorted sands (narrow size distribution) than for poorly sorted sands (wide size distribution). Ultimately, poorly sorted sands may require criteria different from those for well-sorted sands for slot-size selection. Flow rates are also crucial for managing sand production because a critical pressure gradient is required for initiating sand production and maintaining continuous sand production. The critical pressure gradient decreases as the slot width or confining pressure increases. Large permeability increases were observed in the sand-production region. Persistent sand production led to the growth of a channel and/or the presence of a dilated zone that had an elliptic shape.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".