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Record W2068021166 · doi:10.2118/127931-pa

A Review of Screen Selection for Standalone Applications and a New Methodology

2011· review· en· W2068021166 on OpenAlexaff
Rajesh A. Chanpura, R. M. Hodge, J. S. Andrews, E. P. Toffanin, Terje Moen, M. Parlar

Bibliographic record

VenueSPE Drilling & Completion · 2011
Typereview
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsConocoPhillips (Canada)
FundersConocoPhillips
KeywordsSelection (genetic algorithm)Computer scienceProcess engineeringEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Summary Standalone screens (SASs) in open hole can provide highly reliable sand-control completions at a lower cost and with less operational complexity than other openhole sand-control completions and can provide long-term productivity performance comparable to other openhole completions when applied in the "right environment with the right procedures." Although many in the industry would agree with the preceding statement, there is no consensus on what the right environment is and what the right procedures are. Even when there is agreement on the applicability of SASs for a particular sand-size distribution, there are considerable differences in the recommended screen type and screen opening between various laboratories. In this paper, we critically review the various laboratory testing procedures used in the industry and the interpretations made to evaluate screen performance and screen selection for SAS applications. We demonstrate that the way some of the laboratory tests are performed makes them biased toward one type of screen (wire wrap) and that some are interpreted without sufficient information such that they almost always favor another type of screen (premium mesh). We show that severe screen plugging with clean formation sand is almost never an issue and that the probability of screen plugging because of other factors can be minimized when proper precautions are taken. We propose that candidates for SAS applications be initially selected on the basis of sand-retention performance, with the final selection confirmed on the basis of screen/sand pack permeability measurements. In addition, on the basis of approximately 185 laboratory tests performed on various types of wire-wrap (6 to 16 gauge) and premium mesh (60 to 600 μm) screens for unconsolidated sands and using a set criterion for sand retention, we conclude that many of the currently used criteria in the industry for selection between gravel packing and SAS are highly conservative and unduly limit the possible application of SASs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.016
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0080.006
Science and technology studies0.0010.002
Scholarly communication0.0030.004
Open science0.0030.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.003

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.

Opus teacher head0.162
GPT teacher head0.361
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations85
Published2011
Admission routes1
Has abstractyes

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