Flow Control Devices in Steam Assisted Thermal Applications: A Way to Optimize Both Steam Injection and Fluids Production
Bibliographic record
Abstract
Abstract Flow Control Device (FCD) completions in steam assisted thermal applications have been implemented in several places: Canada, California, China, Oman and Colombia, among others. Such completion configurations have been more common in recent years to mitigate or avoid uneven and/or improper steam placement and steam breakthrough, which are some of the critical issues operators have experienced in these developments. This study presents different FCD technologies designed to optimize the steam injection and fluids production for diverse steam assisted applications including SAGD, CSS and Steam Flooding. Three FCD technologies are introduced: (i) supersonic steam injection FCD, (ii) steam choking FCD and (iii) multi-directional FCD. Extensive Computational Fluid Dynamic (CFD) simulations, analytic near-wellbore simulations and flow loop testing were conducted to evaluate the performance of the three technologies: (i) the supersonic steam injection FCD showed a high pressure recovery (therefore, less upstream pressure requirements) and a reduction of the cumulative steam-oil ratio, (ii) the steam choking FCD demonstrated the highest steam choking capability for these type of devices and (iii) the multi-directional FCD showed promising results for CSS applications to allow for supersonic steam injection during the injection phase and steam choking capabilities during the production phase Common FCD deployment risks such as erosion, scaling potential and high pressure drops were reviewed to provide the reader with a high level understanding of the factors which could induce these issues. Finally, field data where FCD completions have been installed is presented to compare the FCD wells performance versus conventional well designs and illustrate the success of these completions strategies. Keywords: flow control devices, supersonic steam injection, steam choking
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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 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".