The Evolution of the Design, Evaluation, Implementation and Monitoring of Standalone Screens
Bibliographic record
Abstract
Abstract This paper aims to comprehensively introduce the historical, present, and future landscape of standalone screens’ design, and evaluation. The evolution of sand control screen design, testing, and implementation is explored, with a focus on its role in overcoming the challenges posed by heterogeneous geological formations and complex EOR strategies. Field case data will be used to illuminate the need for a more scientific approach to sand control engineering. As operators venture into increasingly complex and heterogeneous formations, precise sand control engineering becomes imperative. This paper will delve into the historical standalone screen design, highlighting its advantages and drawbacks. Through field case examples, we will underscore the necessity for a more scientific approach. A comprehensive sand control design cycle—from information acquisition and lab testing to data analytics, manufacturing, field installation, monitoring, and post-performance analysis—will be elucidated. Since 2017, the first attempt to optimize the standalone screen design has led to the development of a sand control best practice. The best practice outlines the guidelines and steps that must be followed on the sand control design, evaluation, and monitoring for future pads. The guidelines have been successfully implemented in the field with remarkable success and no failure to report to date. The guidelines specify the sampling method, the testing requirement to identify the sand variation within the studied sandbox, and selection criteria and evaluation testing through sand retention testing. The guidelines specify the manufacturing tolerances for each screen type. Additionally, the monitoring details for wells completed with sand control and adjustment of the best practices based on the monitoring data are outlined in the guidelines. This paper shares the rich history and lessons learned from sand control design from two fields where different standalone screens are used as the primary completion method. Designing and optimizing the sand control screens for wells is a formidable challenge, given the intricate nature of downhole environments. This paper conveys lessons and learns from field case studies to the industry, shedding light on the complexities and offering guidance for future sand screen engineering. The insights shared aim to contribute to the continuous improvement and advancement of sand control practices in the global hydrocarbon recovery sector.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".