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Record W4255338283 · doi:10.2523/71671-ms

Extreme Overbalance Perforating and One-Trip Perforate and Gravel Pack-Combination of Two Techniques for Successful High Rate Gas Well Completions in the Ha'py Field

2001· article· en· W4255338283 on OpenAlexaff
E. Vickery, Leo Hill, Forgenie Victor, McCollin Roger, Fahmy Helmy, Carl Butler, Mohamed Omar

Bibliographic record

VenueProceedings of SPE Annual Technical Conference and Exhibition · 2001
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsCarbon Engineering (Canada)
Fundersnot available
KeywordsNatural gas fieldPetroleum engineeringField (mathematics)Computer scienceGeologyEngineeringWaste managementMathematicsNatural gas

Abstract

fetched live from OpenAlex

The Extreme Overbalanced Perforating (EOB) technique has been successfully applied in a variety of locations around the world both in ‘hard rock’ and ‘soft rock’ formations. In certain applications, it offers definite advantages over other perforating techniques. One-Trip Perforating and Gravel Pack technology also has been successfully applied at various locations with definite advantages over other ‘soft rock’ cased hole completion techniques. Prior to the completion of three wells in the Ha'py field, these two technologies had never been applied concurrently.This paper describes the teamwork that was required between the operating company and the service providers to properly combine these methods and ensure their successful application in the field. The authors outline some of the obstacles encountered and describe how the tools and procedures were designed for success.During the three well completions in the Ha'py field, some problems were encountered that required modifications to operations. The lessons learned from these problems and how they were applied to subsequent operations are also addressed.

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 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 score0.167
Threshold uncertainty score0.394

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 communication0.0000.000
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 head0.022
GPT teacher head0.252
Teacher spread0.230 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations0
Published2001
Admission routes1
Has abstractyes

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