Design and Field Evaluation of Tubing-Deployed Passive Outflow-Control Devices in Steam-Assisted-Gravity-Drainage Injection Wells
Bibliographic record
Abstract
Summary The integration of horizontal wells and thermal-oil-recovery methods, such as steam-assisted gravity drainage (SAGD), has enabled the economic exploitation of extraheavy-oil resources, mainly in Canada. The use of passive outflow-control devices (OCDs) in SAGD wells adds steam-injection points along the horizontal wellbore, influencing steam placement and chamber growth, thus potentially reducing the steam/oil ratio, minimizing productivity uncertainty, and accelerating production. To design OCD installations in SAGD, we need to address two main aspects. The first is the interface between horizontal-wellbore hydraulics and reservoir injectivity, which allows for the determination of the number and location of steam-injection points for improved performance. The other aspect is the design of the OCD itself, which involves selecting and configuring the device with a hydraulic performance that is fit for purpose. For this study, we will focus specifically on straight-orifice-choke passive OCDs. This paper presents a comprehensive design methodology for tubing-deployed passive OCDs in SAGD. The completion design is carried out with a steady-state model of the injection well from a commercial thermal wellbore simulator. The field-performance evaluation of tubing-deployed passive OCDs is critical for verifying the effectiveness of the design methodology and the hydraulic performance of the devices under real field conditions. The field evaluation is performed by history matching the injection pressure vs. the steam-rate data with a model developed in the thermal wellbore simulator. A dynamic pressure gradient (under flowing conditions) inside the injection string carrying the OCDs is obtained with a temperature log, taken with fiber-optic technology, in which the temperature data are converted to pressure by virtue of the properties of saturated steam. This method for measuring the dynamic pressure gradient during steam injection is novel for the SAGD industry. The hydraulic field performance of the OCDs was matched successfully with the simulated model, which indicates the effectiveness of the design methodology, the field-performance-evaluation techniques, and the OCDs in delivering the desired amount of steam at each location.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".