Slurrified Heavy Oil Reservoir Extraction (SHORE): A Non-Thermal, <i>in situ</i> Recovery Method
Bibliographic record
Abstract
Abstract The oil sands of Canada are a rich resource whose extraction faces many challenges. The most common processes for recovering in situ resources (i.e. too deep to mine) involve heating the reservoir to reduce the heavy oil or bitumen viscosity to allow it to flow to wellbores where it can be produced. This paper presents an alternate concept for recovery of these resources that does not require heat to mobilize the bitumen and that is especially well suited to reservoirs that are too thin or too geologically complex for economic thermal recovery. The process utilizes water injection to "condition" a reservoir interval sufficiently to relieve the overburden stress on the oil sand and increase its porosity and permeability. Establishing a pressure gradient between a set of injector and producer wells allows the production of a bitumen-sand-water slurry as the pressure gradient established overcomes the friction holding the reservoir sand in place. This produced slurry is then processed at the surface to extract the bitumen and the cleaned tailings are re-injected back into the reservoir to aid in the sweep of the in situ sand, support the overburden, and dispose of the tails. We have developed a first principles numerical model of the process that fully accounts for fluid flow and sand flow under reservoir conditions to simulate and understand the process. We have also developed a large scale (2 meter diameter sand pack) laboratory system to demonstrate the technical feasibility of the process under reservoir conditions. The technology is still in the early stages of development, but the laboratory and numerical modeling efforts demonstrate promising technical potential of the process at a field-scale. The ability of the process to work in thinner and more geologically complex reservoirs than other in situ processes, and with lower CO2 and surface footprints than thermal and mining processes, could make this an attractive alternative recovery process for shallow to intermediate depth, in situ bitumen resources.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".