A Critical Review of Sand Control Evaluation Testing for SAGD Applications
Bibliographic record
Abstract
Abstract This paper presents a critical review of current evaluation techniques for the selection and design of sand control devices (SCD) for Steam Assisted Gravity Drainage (SAGD) wells. With the industry moving towards exploiting more difficult reservoirs, there is a need to review the current testing methods and assess their adequacy for sand control evaluation for different operational and geological conditions. In addition to a critical review of existing sand control testing approaches for SAGD, the paper also discusses the testing parameters in previous studies to evaluate their representativeness of the field conditions in terms of interstitial seepage and viscous forces, and flow geometry. Moreover, the paper reviews the analysis and results of sand control testing in the literature and assesses the sand control design criteria in terms of the level of acceptable sand production and plugging. Furthermore, the review evaluates the suitability of the sample size, sand preparation techniques, representation of the SCD in the testing, and experimental procedures. The review shows variations in the existing sand control testing in SAGD, in terms of not only approach, sand control representation, and sample size, but also regarding operational test conditions, such as flow rates and pressures. Ideally, large-scale pre-packed tests that include the effects of temperature and radial flow geometry would more closely emulate the actual conditions of SAGD wells than most existing tests allow. High temperatures may affect sanding and plugging through changes in wettability, permeabilities, and mineral alterations. Further, the varying velocity profile in radial flow towards the SCD influences the fines migration pattern differently from the linear-flow conditions in the existing Sand Retention Tests (SRT). However, large-scale radial-flow tests are constrained by cost and complexity. Most SRT experiments have employed high flow rates, exceeding the equivalent field rates. Utilizing realistic rates for the tests and appropriately capturing the actual fluids ratios, water cuts and steam breakthrough scenarios can improve the quality of testing data. Accordingly, existing SRT experiments can be designed to incorporate, if not all, but some of the relevant physics in SAGD by employing representative viscosities, flow rates, fluid properties and ratios, stress conditions and obtain suitable live and post-mortem measurements. This critical review compiles various aspects of current sand retention tests and evaluates their applicability to SAGD well conditions. It serves as a starting point for future research by providing an overview of existing testing methods, highlighting the strengths and opportunities for improvements.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".