Using Prepack Sand-Retention Tests (SRT's) to Narrow Down Liner/Screen Sizing in SAGD Wells
Bibliographic record
Abstract
Abstract Due to the unconsolidated nature of the Athabasca Oil Sands in the Western Canadian Sedimentary Basin, it is common practice to complete Steam Assisted Gravity Drainage (SAGD) well pairs with the use of standalone screens (SaS's). The sizing of the liner/screen types are commonly determined by: first, getting a particle size distribution (PSD) by dry-sieve analysis and/or Laser Particle Sand Analysis (LPSA); and second, running a number of prepack Sand-Retention Tests (SRT's). In this Nexen Long Lake study, five batches of unconsolidated McMurray Formation sand were collected from six different cored wells. Using these batches of sand, more than twenty SRT's were run with a variety of fine-tuned modifications to the traditional test protocols to best duplicate fluid production conditions as observed in the field. Some of these modifications included: altering the injected fluid/gas rate and reconfiguring the order in which the fluids / gas were injected. The SRT results were then plotted to identify if they passed the general criteria for a successful sand control device design. Unexpectedly, many of the SRT results did not meet the pass criteria for solids production and therefore, altered the direction in which future tests were run. However, when reviewing the solids production with the cumulative fluids / gas injected, the outcome commonly reverted to being favourable. Understanding the laboratory derived test results, and how they applied to the field, was instrumental in the lab testing process. By redefining the tests results and pass/fail criteria, based upon observed in-situ production conditions, it was possible to make both qualitative as well as quantitative analysis and therefore, more confidently decide on the optimal reservoir completion type. The study discusses an alternative approach to interpretation of conventional SRT results relative to observed field production conditions and how ultimately, this analysis influenced the choice of liner/screen sizing selected for implementation in future Long Lake field development projects.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".