Sand on Demand: An Approach to Improving Productivity in Horizontal Wells under Heavy Oil Primary Production - Part II
Bibliographic record
Abstract
Abstract The cold production recovery process is successful in vertical wells in western Canada. In this process, large amounts of sand are produced on a continuing basis along with heavy oil. Attempts at cold production in horizontal wells have not been particularly successful. When sand production has been generated in horizontal wells, these wells have tended to become plugged with sand. 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., managed) sand production strategies. Specifically, the effects of slot size, confining stress, fluid velocity and sand grain sorting on sand production were investigated. The results indicate that slot size selection is critical for establishing "sand on demand". From the experiments, a correlation between slot size and controlled sand production was found for well sorted sands. This correlation should allow for the specification of appropriate slot sizes for target reservoirs containing well sorted sands. In the experiments, when flow rates resulted in low but persistent sand production, channels and/or elliptical dilated zones were created that greatly enhanced the effective permeability near the slot. This observation suggests that producing at low and steady sand cuts for a long period of time might bring two benefits: a way to transport the sand out of the well without causing plugging, and the creation of high permeability channels or zones that can improve production from the reservoir. To summarize, if the appropriate slot size was combined with the right draw down rates, controlled sand production could be achieved with attendant significant increases in permeability. This suggests that substantially increased oil production rates could be achieved from horizontal wells if sand production rates could be maintained at low but persistent levels.
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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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".