A mathematical model for compressible flows of ideal gases through inflowcontrol devices used in enhanced oil recovery
Bibliographic record
Abstract
Inflow control devices (ICDs) are used widely in Alberta to increase the oil yield and decrease both the economic and environmental costs of steam assisted gravity drainage (SAGD) based oil extraction.An ideal ICD is highly fluid selective: it creates pressure drop primarily through mechanisms that are sensitive to the fluid properties unique to undesired fluids, while creating minimal pressure drop through mechanisms strongly dependent on the fluid properties of desired fluids.Industry desires better criteria for comparing ICD designs that capture the ICD's ability to be fluid selective towards allowing viscous bitumen while restricting water and gases, such as steam and butane (solvent), which tend to flow at higher velocities.This paper proposes a mathematical model using two loss criteria to describe the performance curves of various simple ICDs operating in the compressible flow regime with ideal gases.The variability of these two loss criteria as functions of the reservoir pressure, molecular mass, and specific heat ratio of the ideal gas under the expected range of reservoir conditions is explored and compared to theoretical predictions and qualitative trends.The two criteria are linked to flow behavior in different parts of the ICDs, and the limits of their applicability are discussed.The ICD performance curves that the mathematical model is based on were obtained using a steady state approach in ANSYS CFX with total energy and SST turbulence models.To show how the proposed compressible flow model could be used in industry, example comparisons between various simple ICDs are performed.In the first example comparison, ICDs are first sized to have the same flow resistance rating (FRR) of 0.8, then the flow rates of steam through each device are predicted using the proposed mathematical model.In a second example, following a similar method as an in-house code used by our industry partner, the required pressure drop to achieve a target mass flow rate through the best ICD design is calculated.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".