Steam Flow Tests for Comparing Performance of Nozzle, Tube, and Fluidic Diode Autonomous ICDs in SAGD Wells
Bibliographic record
Abstract
Abstract Many operators are considering installation of flow-control devices (FCDs) in horizontal wells to improve steam-oil ratios (SOR) in steam-assisted gravity drainage (SAGD) recovery processes in heavy oil/bitumen reservoirs. The flow-control devices are used to help balance both the steam injection and fluid production in order to increase the oil recovery efficiency and use the full length of the horizontal wells. SAGD injector and producer horizontal wells are typically 3 to 6 meters apart, vertically. Because of this proximity, steam breakthrough to the producer well is possible. In order to reduce the steam loss following a steam breakthrough, operators typically try to slow the total rate of production. This paper will discuss the testing of passive inflow control devices (ICDs) and an autonomous inflow control device (AICD) in a steam-flow test loop along with testing results to help control the breakthrough of steam. Heated water flow through the ICDs and AICDs was used as the baseline case. Saturated steam simulating steam flow conditions (pressure and temperature) in a SAGD environment was flowed through the devices at two different temperatures, and the resulting flow rates were recorded at several pressure differentials. The laboratory flow testing has helped demonstrate how the ICDs and AICDs can either help prevent steam breakthrough from occurring or limit the rate of steam breakthrough in the zones of concern. By limiting the flow rate of steam breakthrough, the flow control devices will also help to protect the sand screen from erosion caused by high velocity flow.
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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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".