Evaluation of Inflow Control Device Performance using Computational Fluid Dynamics
Bibliographic record
Abstract
Abstract In steam injection thermal recovery, it is essential to have a uniform flow to improve the recovery and to avoid the localized steam breakthrough which could lead to damage to well completion. In this paper, we propose three quantitative criteria to assess the performance of inflow control devices (ICD) based on computational fluid dynamics (CFD) modeling. The new performance criteria are exemplified in the evaluation of a few basic ICD designs. To evaluate the response of the ICD to flow rate and fluid type, three new performance criteria, defined as (1) quadratic flow coefficient, (2) viscosity coefficient, and (3) erosion potential, are proposed and evaluated based on a set of CFD simulations. The first criterion measures the flow rate response and the ability of the ICD to restrict high velocity flow, the second quantifies the viscosity sensitivity, and the third predicts the potential for erosion in the device. Four different liner deployed ICD designs, based on two passive design types (nozzle and channel) and one autonomous design type (Tesla flow diode), were analyzed using a rigorous CFD model. The model includes the surrounding slotted liner and inner tubing to identify any interactions of the ICD with the surrounding completion. The CFD model has been verified for grid and domain independence and it was applied to a range of flow rates representative of the field condition. In addition, simulations were run for a range of single-phase incompressible fluids with varying viscosities. Using the newly proposed criteria, the ICDs were evaluated and compared. The comparison shows that, of these devices, the diode does the best job of restricting the flow at high flow speeds and low viscosities. At high viscosities, such as in the case of oil, the diode is the least restrictive device. Although the two straight nozzles tested are slightly worse at restricting the flow, they have the lowest erosion potential. Based on this comparison and the proposed criteria, the channel design performs poorly. At low viscosities it does not sufficiently restrict the flow, and at high viscosities it overly restricts the production of oil. It also has a high erosion potential, because of the steep entrance angle. In this work, a new set of quantifiable criteria are defined and assessed that allow multiple aspects of different ICD designs to be compared simultaneously. Overall, these three criteria give a highly sensitive, quantitative means of comparing ICD designs. With these three criteria together, a more comprehensive comparison can be made in support of selection and improvement of ICDs.
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How this classification was reachedexpand
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 | 0.001 | 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) | 0.000 | 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 teacher head, 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".