Thermal Well Screen Plugging: Revisiting an Old Challenge, Part 1: Role of Drilling Fluids in Screen Impairment
Bibliographic record
Abstract
Abstract This paper presents results from novel tests investigating screen plugging and the factors that influence it. The study focuses on sand retention and flow convergence into screens, assuming formation collapse. Controlled experiments were conducted to evaluate the performance of different screens and the role of sand in plugging. The tests also explore how drilling and completion processes affect screen performance and plugging behavior. A novel test procedure was developed to simulate sand retention on screens. Screens with retained sand were placed in a flow apparatus to study multiphase flow behavior. A mix of sand and fines slurry was used to evaluate screen and near-screen plugging. The effect of mud and filter cake was assessed by allowing cake formation on the sand face before flowing. Additionally, the influence of temperature—up to 200°C—was examined to understand thermal impacts on screen plugging mechanisms under realistic downhole conditions. This study utilized three Particle Size Distribution (PSD) classes from the McMurray Formation to represent common variations in thermal project sands. A common polymeric mud, representative of the mud system used in thermal drilling, was employed. The thickness of the mud cake varied significantly among the three sands and was directly related to their permeability, with finer sands forming denser cakes that reduced retained permeability by over 55% in the lowest permeability class. Experiments during the drawdown phase revealed that wire-wrapped screens outperformed punch screens in permeability recovery due to enhanced channeling, with coarser PSDs showing better restoration (up to 72% retention). Thermal exposure at 200°C, intended to break down polymers, instead densified the cake with residue deposition, reducing post-steaming permeability by 5-30% depending on PSD, though wire-wrapped screens with 100-150 μm slots mitigated losses. Plugging occurred primarily at the sand-cake interface, emphasizing the need for near-screen management. These findings support preventive measures to minimize plugging and optimize thermal performance. This study aims to help engineers understand the key factors influencing screen plugging and the resulting high differential pressure drop between injectors and producers. The insights gained support the implementation of preventive measures to avoid or minimize screen plugging, improving overall system performance and reliability.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".