Critical Oil Rate and Well Productivity in Cold Production From Heavy-Oil Reservoirs
Bibliographic record
Abstract
Summary Cold heavy-oil production with sand (CHOPS) has been widely used for recovering heavy oil from unconsolidated sandstones (UCSs). Although this technology is considered to be mature in some oil fields in Canada, there are some technical issues that need to be addressed when this technology is transferred to fields in other parts of the world. These issues are primarily related to the variations in local geological and reservoir conditions. One of the concerns is whether the designed well production rate is high enough to self-clean the wellbore against sand accumulation. During planning of CHOPS completions, it is imperative to know if the designed fluid-production rate will be adequate to carry sand to surface, especially when horizontal wells are employed, where a significant amount of sand can accumulate in the horizontal wellbore that can kill the well. However, it is not clear what constitutes the "adequate" fluid-production rate. A theoretical investigation of sand transport in heavy oil was conducted in this study. A critical fluid-production rate was defined to quantitatively describe the "adequate" production rate required to carry sand to surface in vertical, inclined, and horizontal wells. Also developed in this study is a CHOPS-well deliverability model based on self-stimulation of reservoir and oil/water/gas/solid four-phase flow in the production string. Combined use of the critical-production-rate model and the well-deliverability model allows for optimal selection of pumps that will ensure the smooth production of fluids in CHOPS operations. This paper provides petroleum engineers with essential knowledge and information for planning CHOPS well completions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".