Method to Improve Thermal EOR Performance Using Intelligent Well Technology: Orion SAGD Field Trial
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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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it