Design Optimization of Slotted Liner Completions in Horizontal Wells: An Analytical Skin Factor Model Verified by Computational Fluid Dynamics and Experimental Sand Retention Tests
Bibliographic record
Abstract
Abstract The term skin is used to describe pressure drop caused by a flow restriction near the wellbore. The skin factor of wells completed using slotted liners can be explained by a number of phenomena including: the flow across the slots, flow convergence towards slots, near wellbore permeability, and occlusion of liner open area due to corrosion and scale deposition. This paper introduces an analytical skin model for the slotted liner, which incorporates these phenomena, and can be used to optimize the slotted liner design. The introduced analytical model was verified by physical and Computational Fluid Dynamics (CFD) models. The proposed analytical skin factor model for slotted liners is based on slot width, slot density, the spatial distribution of slots, and near-liner permeability. The model also incorporates partial plugging of slots. The model is validated using experimental Sand Retention Testing (SRT) data. A series of SRT experiments were conducted at different flow rates for two Particle Size Distributions (PSD) from the McMurray Formation in Northern Alberta. The experiments were also modeled by the CFD to better understand the flow dynamic near the liner. Results of the analytical model and experimental tests were generally in agreement. However, results of the analytical model deviate from experimental tests for narrow slots and high flow rates. In these cases, the analytical model predicts smaller skin than the experimental tests. For cases related to narrow slots and higher velocity the pore plugging close to the liner is significant which was not modeled in the analytical model. Moreover, for very fine sand (low permeability) sand-pack the deviation from the experimental results is higher in comparison with medium uniform sand (higher permeability) sand-pack. CFD simulations showed the effect of the slot width on the depth of the convergence zone, which is not included in the analytical model. Since the analytical model follows the experimental results for common flow rates in thermal production, the model could be used to assess the skin for different possible designs and choose the best slot specifications that minimize the skin. This paper presents the details of an analytical model for the skin factor verified by experimental data and CFD simulation. This analytical model can be used to optimize the liner specification for the best flow performance. This paper also outlines the limitations of the analytical models for calculation the skin/pressure drop.
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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 | 0.000 | 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".