Risk Assessment in Sand Control Selection: Introducing a Traffic Light System in Stand-Alone Screen Selection
Bibliographic record
Abstract
Abstract Sand control and sand management require a rigorous assessment of several contributing factors including the sand facies variation, fluid composition, near-wellbore velocities, interaction of the sand control with other completion tools and operational practices. A multivariate approach or risk analysis is required to consider the relative role of each parameter in the overall design for reliable and robust sand control. This paper introduces a qualitative risk factor model for this purpose. In this research, a series of Sand Retention Tests (SRT) was conducted, and results were used to formulate a set of design criteria for slotted liners. The proposed criteria specify both the slot width and density for different operational conditions and different classes of Particle Size Distribution (PSD) for the McMurray oil sands. The goal is to provide a qualitative rationale for choosing the best liner design that keeps the produced sand and skin within an acceptable level. The test is performed at several flow rates to account for different operational conditions for Steam Assisted Gravity Drainage (SAGD) and Cyclic Steam Stimulation (CSS) wells. A Traffic Light System (TLS) is adopted for presenting the design criteria in which the red and green colors are used to indicate, respectively, unacceptable and acceptable design concerning sanding and plugging. Yellow color in the TLS is also used to indicate marginal design. Testing results indicate the liner performance is affected by the near-wellbore flow velocities, geochemical composition of the produced water, PSD of the formation sand and fines content, and composition of formation clays. For low near-wellbore velocities and typical produced water composition, conservatively designed narrow slots show a similar performance compared to somewhat wider slots. However, high fluid flow velocities or unfavorable water composition results in excessive plugging of the pore space near the screen leading to significant pressure drops for narrow slots. The new design criteria suggest at low flow rates, slot widths up to three and half times of the mean grain size will result in minimal sand production. At elevated flow rates, however, this range shrinks to somewhere between one and a half to three times the mean grain size. This paper presents novel design criteria for slotted liners using the results of multi-slot coupons in SRT testing, which is deemed to be more realistic compared to the single-slot coupon experiments in the previous tests. The new design criteria consider not only certain points on the PSD curve (e.g., D50 or D70) but also the shape of the PSD curve, water cut, and gas oil ratio and other parameters.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".