Sand Screen Design and Optimization for Horizontal Wells Using Reservoir Grain Size Distribution Mapping
Bibliographic record
Abstract
Abstract The primary goal of screen design for steam assisted gravity drainage (SAGD) operations is to prevent the entrance of the poorly or unconsolidated sand into the production flow stream, which could cause serious damages to the downhole/surface facilities. Current screen design approach fits one screen opening size for the entire length of the well, which leads to a conservative screen opening to avoid sand production along the well. This study introduces a new approach to design the screen opening, considering different opening sizes along the horizontal well. The proposed workflow in designing the optimum screen opening relies on well logs and core analysis to map the grain size distribution within reservoir through a geostatistical approach. Considering the horizontal well path and the changes in liner length due to installation and thermal loads, we design the screen aperture size to optimize the screen selection based on sand facies present in different sections of the wellbore. This enables us to provide different screen opening for different sand facies along the horizontal well. The new approach provides a more detailed design for screen opening for the horizontal well according to the sand size distribution within the reservoir instead of trying to fit one opening size for the entire horizontal section. It also considers the thermal expansion of the joints. This approach designs the screen opening for different sand facies along the horizontal well in a way which obtains the highest productivity and lowest produced sand. This paper provides a novel workflow for the design and optimization of screen for horizontal wells, which could be used to optimize the design of different standalone screens such as slotted liner, precise punch screen (PPS) and wire wrapped screen (WWS).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".