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Record W2015272519 · doi:10.2118/74496-ms

A New TAML Level 3 Multilateral System Improves Capabilities and Operational Efficiencies

2002· article· en· W2015272519 on OpenAlexaboutno aff
Steven Fipke, Jim Oberkircher

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsPaceProcess (computing)Computer scienceSystems engineeringTelecommunicationsEngineeringOperating systemGeology

Abstract

fetched live from OpenAlex

Abstract In recent years there has been a dramatic increase in the pace of the evolution of multilateral systems. Many systems with new features and improved functionality have been introduced which have enhanced the success and growth of multilateral technology. The MACH-3™ system is among the latest of these offerings. This innovative hybrid system is an example of RMLS™ technology that has been re-designed to provide a simpler and faster method of completing TAML Level 3 multilateral junctions. This system will greatly reduce the amount of rig time required for multilateral junction construction by simplifying the installation process and minimizing the number of trips to complete. It also provides increased functionality with large diameter, selective re-entry access to either the lateral or main well bore. Primarily designed for heavy oil applications where the cost of installation is a major issue, the system has potentially far reaching applications in this and other markets. The paper describes the mechanical and operational principles of the system in its potential applications. The basic functionality of the MACH-3™ system will be analyzed, and some simple guidelines and limitations will be put forward for the selection of a Level 3 multilateral system. These guidelines are based on some of the lessons learned from multilateral experience in Canada, Venezuela and the U.S.A.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.560
Threshold uncertainty score0.278

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 head0.017
GPT teacher head0.191
Teacher spread0.174 · 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

Citations8
Published2002
Admission routes1
Has abstractyes

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