Using a New Intelligent Completion Strategy to Increase Thermal EOR Recoveries–SAGD Field Trial
Bibliographic record
Abstract
Abstract A completions strategy has been developed for improving both steam injection and production conformance in a thermal- enhanced oil recovery (EOR) project by using intelligent completion technology that incorporates interval control valves (ICVs), well segmentation, and instrumentation. The initial field trial is ongoing in the injector of a Northern Alberta steam-assisted gravity drainage (SAGD) well pair. Depending on the level of heterogeneity present in the reservoir, the application modeling shows that a 45% reduction in the steam-oil ratio and an almost 70% increase in recovery can be achieved in a SAGD process when both improved injection conformance and producer differential steam-trap control can be applied in a segmented horizontal well pair. A cost-effective intelligent completion solution to achieve this segmentation and control has the potential to add substantial value to field developments resulting in increased energy efficiency and oil recovery through improved steam conformance. The method being developed is also applicable to a wide range of other thermal EOR processes such as cyclic steam stimulation (CSS), steam drive and variations, which include those processes involving solvent additives. The initial field deployment in the injector well was conducted primarily to prove the technology, to demonstrate the feasibility of modifying the steam distribution, and to determine best practices for future developments. A successful installation and commissioning of the intelligent completion has validated the technology substantially. Lessons learned are highlighted. Early injection test results and data show a significant increase in the understanding of the injection and production behavior in the well pair. The intelligent completion technology under trial and proposed developments should enable more extensive use of downhole measurement and control in thermal EOR projects than has been possible to date. This paper discusses the development of the completion technology, its applicability to thermal conditions, initial field trial results and the plans for further development. A test program to optimize the distribution of the steam injection in the well is underway, and the results to date also will be discussed.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".