Heavy-Oil Production Enhancement by Encouraging Sand Production
Bibliographic record
Abstract
Abstract Dramatic increases in oil production rates have been achieved in many Canadian heavy oil reservoirs. These reservoirs are 30% porosity unconsolidated sandstones with oil ranging from 500 to 12,000 cP viscosity. Furthermore, many of these reservoirs have proven to be almost impossible to exploit economically with horizontal wells or with thermal processes. After reviewing the mechanics of CHOP (Cold Heavy Oil Production), the production history of the Luseland Field in Saskatchewan is reviewed. This is almost a unique case history because conventional production, horizontal wells, and CHOP have all been attempted in a small geographic area. Encouraging sanding resulted in over a four-fold increase in oil production rate and a total extraction ratio now just in excess of 11% overall. An aggressive CHOP program implemented after many years of conventional production and after a six-well horizontal production program achieved this increase. Conventional production was marginally economic, but the horizontal wells were failures. Several other case histories are summarized to demonstrate that CHOP wells in these reservoirs are usually just as productive as much more costly horizontal wells. We believe that CHOP technology is a far better option than thermal stimulation or horizontal wells in many cases where the reservoir state and rock properties are suitable.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".