Bibliographic record
Abstract
Abstract Most of Canada's trillion barrels of petroleum consists of bitumen, and to a lesser extent, heavy oil. This total may be compared with the total Canadian production of light-medium crude oil to date, which is only 12 billion barrels. Mining is effective for the production of bitumen, but is limited to the minor fraction of the resource that is shallow; also mining involves significant environmental difficulties. The challenges of efficient in situ production are like those for other petroleum production activities:to find and define suitable reservoirs,to create conditions for oil to flow at economic rates, andto drain the reservoir systematically to obtain high recoveries. This paper discusses the following in terms of production rate, recovery, energy requirements and economic factors. Each topic is a step, sometimes a sideways step, in the search for a means to achieve high rates and recovery within the bounds of economic constraints.Cold production using vertical wells and horizontal wells.Stimulation by wellbore heating.Cyclic steaming using conventional wells.Steam Assisted Gravity Drainage (SAGD).Steam and Gas Push (SAGP).Cyclic Steaming with horizontal wells.Vapour Extraction (Vapex). Processes resulting in the displacement of oil by gas to a lower horizontal well show the most promise. The viscosity in the region around the horizontal well should be reduced to allow economic rates without gas coning. In SAGD, injected steam heats the oil and fills the reservoir as it drains, and rates of 79 – 159 m3/d (500 – 1,000 B/d) can be achieved with bitumen recoveries greater than 50%. Heat savings can be achieved by building a substantial gas concentration within the depleted region (SAGP). In Vapex, viscosity reduction is obtained by dilution with a olatile solvent; this is a promising approach for lower viscosity heavy oils. Another promising approach is cyclic steaming with horizontal wells, combined with gas addition to the steam to maintain drive. Introduction As conventional oil reserves become depleted, interest continues to grow in the improved recovery and utilization of Canadian tar sands and heavy oil. The resources are enormous in magnitude, and there have been great strides in technology. One approach, the mining and upgrading of shallow Athabasca deposits has, with the success of the Suncor and Syncrude projects, already become a major source of Canadian oil. Major expansions to both of these projects, as well as other new tar sand mining projects are underway. While mining overcomes the problem of moving the oil to the surface and of obtaining high recoveries, it requires the handling and disposal of vast amounts of solids and sludge, and it is only economic for the shallowest of deposits. The major part of the Canadian oil sand resource is too deeply buried for mining to be practical. This paper is concerned with the recovery of bitumen and heavy oils by in situ methods, i.e., by means of wells drilled from the surface.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".