An Experimental Investigation into Gravel Pack Performance in Steam-Drive Operations
Bibliographic record
Abstract
Abstract Open hole gravel pack (OHGP) has been broadly used for completion of steam-drive production wells. However, some failures have been observed with the gravel pack in such complex completions. This paper aims to better understand the OHGP performance in steam-drive production wells and examine the performance of rolled-top and straight-cut slotted liners using a large-scale Sand Retention Testing (SRT). A large-scale SRT facility was developed to investigate the performance of the gravel pack in two-phase flow regime. The testing set-up allows for co-injection of oil and brine at controlled flow rate and water cut level to emulate different scenarios for two-phase flow across the gravel pack and sand screen/liner. Testing measurements included produced sand, absolute pressures, and differential pressure drops across the slotted liner, gravel pack, gravel-sand pack interface and sand pack. The test procedure and test matrix were designed to enable an accurate assessment of the gravel pack and slotted liner performance for different fluid flow scenarios. Rolled-top and straight-cut slotted liner coupons were used for this study. Test results showed negligible sand production for both rolled-top and straight-cut slotted liners, however the produced sand was slightly higher for the rolled-top profile. The pressure drop across the rolled-top liners were smaller than the straight-cut liners based on the analytical analysis presented in this study. The results have also shown that a key factor in gravel packing performance is the ratio of the gravel pack size to the formation sand (sand pack) size. Larger gravels allow an easier production of the fines, while smaller gravels may trap the fines and be plugged over time. This work provides a robust testing facility to address the gravel pack performance in steam-drive producer wells. The results help the engineers with gravel pack and sand control design and an evaluation for the entire wellbore life.
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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".