Method to Improve Thermal EOR Performance Using Intelligent Well Technology: Orion SAGD Field Trial
Bibliographic record
Abstract
Abstract A method has been developed for improving both steam injection and production conformance in a thermal EOR project by utilizing intelligent well technology incorporating interval control valves (ICV), well segmentation and associated downhole instrumentation. This provides the ability to selectively open and close segmented sections of the well bore and monitor the key parameters of temperature and pressure from surface. The initial field trial is ongoing in the injector of an Orion field SAGD well pair. Development of the completion system suitable for thermal conditions, initial field trial results and plans for further development are described. Modelling shows that, depending on the level of heterogeneity present in the reservoir, an improvement of 20 to 40% in the steam oil ratio and 5 to10 % 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 solution to achieve this segmentation and control has the potential to add substantial value to field developments through improved steam conformance resulting in increased energy efficiency and oil recovery. The method being developed is applicable to a wide range of EOR processes such as CSS, steam drive and variations. The initial field deployment in the injector well was primarily to prove operability of the system in high temperature thermal applications, to demonstrate the feasibility of modifying steam distribution and to learn for future optimization and deployment of the system. A successful installation and commissioning has substantially validated the completion technology. Early injection test results and data provide a significant improvement in the understanding of the injection and production behavior in the well pair. A test program to optimize the distribution of steam injection in the well is underway and the preliminary results are discussed. Lessons learned from the trial are highlighted. The intelligent completion technology under trial, and proposed further developments, should enable more extensive use of downhole measurement and control in thermal EOR projects to improve performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".