Realistic and Promising Technology Measures for SAGD Projects at Low Oil Price
Bibliographic record
Abstract
Abstract Steam assisted gravity drainage (SAGD) process is widely used in super heavy oil and oil sands projects. These projects generally have higher steam to oil ratio and poor economy, partly because un-uniform steam chamber along the horizontal section forms and it is hard to adjust, affecting by reservoir heterogeneity including muddy interlayer and thief zones. Therefore, it is desirable to explore realistic and promising technology measures for SAGD projects at low oil price. In this paper, almost all the technology measures for SAGD projects were extensively and deeply investigated in terms of domestic and foreign reports, literatures and on-site experiences. The available research subjects include Xinjiang Fengcheng and Liaohe super heavy oil projects in China as well as ten oil sands project attached to eight corporations in Canada. Better yet, numerous statistics about technology application are reviewed well-by-well, and field application effects for some technologies were verified by deliberate numerical simulation. Many realistic and enforceable technology measures were systematically analyzed and recommended. Single or multiple stage dilation start-up process assisted by waste water or polymer injection enhanced start-up process significantly. Infilling well pairs or wedge well, and sidetracking horizontal well or fishbone well effectively tapped the unswept remaining oil by steam. The other technologies further improved steam chamber conformance including non-condensable gas co-injection, ICD/FCD technology, differentiated operating pressure strategy, nitrogen plus dispersant foam profile control and other remedial measures, etc. Besides, the present situation and foreground application were summarized and evaluated for several promising new technologies to be studied such as screening low cost mixed solvent to increase solvent recovery, warm solvent gravity drainage (Nsolv) process and in-situ upgrading process assisted by electrical heater or catalytic modification to reduce the capital cost of surface facility, etc. The paper contains some previously unpublished data of practical experiences, and the findings of this investigation add to the knowledge base information related to improving the SAGD performance and economy of super heavy oil or oil sands projects.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".