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Record W2133136048 · doi:10.2118/141456-ms

Best Practices for Multi-zone Stimulation Using Composite Bridge Plugs

2011· article· en· W2133136048 on OpenAlex
Douglas J. Lehr, David D. Cramer

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSPE Production and Operations Symposium · 2011
Typearticle
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsConocoPhillips (Canada)
FundersConocoPhillips
KeywordsBridge (graph theory)Best practiceProduct (mathematics)InstallationComputer scienceWell stimulationSpark plugFossil fuelPetroleum engineeringRisk analysis (engineering)Operations managementEngineeringBusinessGeologyPetroleumMechanical engineeringWaste managementEconomicsManagement

Abstract

fetched live from OpenAlex

Abstract Treatment isolation using composite bridge plugs (CP) has been practiced for about 18 years in North America and continues to be among the most economical ways to stimulate horizontal and multi-layer vertical wells. Despite this long experience, many end users still experience problems in these applications because of sub-optimal choices regarding product selection, run-in and removal options, and unrealistic expectations regarding plug life in downhole environments. This paper will identify best practices for using CPs, based on prior technical papers, field experience, and manufacturers’ published data. These practices maximize the chances of successfully installing and removing CPs in multi-zone treatment applications, improving the economics of oil and gas plays requiring multiple stimulation treatments per well. As multi-zone completion activity increases in regions outside of North America, treatment applications using CPs will also increase in those regions. Realizing the benefits of using CPs in emerging regions will be achieved by learning about best practices from regions with extensive experience in their use.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score0.593

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.118
GPT teacher head0.325
Teacher spread0.207 · 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