MétaCan
Menu
Back to cohort
Record W2064397208 · doi:10.1115/1.3000100

Microhole Coiled Tubing Drilling: A Low Cost Reservoir Access Technology

2009· article· en· W2064397208 on OpenAlexaboutno aff
Kent Perry

Bibliographic record

VenueJournal of Energy Resources Technology · 2009
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsDrillingCoiled tubingPetroleum engineeringWell drillingDrilling rigMeasurement while drillingCompletion (oil and gas wells)Drilling engineeringEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

Although the microhole coiled tubing drilling rigs have been used extensively in Canada, their application in the U.S. has been very limited. In an effort to introduce this technology to the U.S. operators, GTI, with the support of DOE∕NETL, has completed a successful field testing of the coiled tubing microhole drilling technology. In this paper we report results of field testing of the system in 25 wells drilled in the Niobrara unconventional gas play of Kansas and Colorado. The objective of the field test was to measure and document the rig performance under actual drilling conditions. In these tests, a coiled tubing drilling rig (designed and built by T Gipson with Advanced Drilling Technologies Inc.) was utilized. The rig operations have continued to improve to the point where it now drills a 3100ft well in a single day. Well cost savings of approximately 30% over conventional rotary well drilling have been documented. A description of the rig and a summary of its performance in the Niobrara unconventional gas play are included. In addition, an estimate of economic advantages of widespread application of microhole drilling technology in the lower 48 states is presented.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.006
GPT teacher head0.217
Teacher spread0.210 · 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 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

Citations16
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Energy Resources TechnologySame topicDrilling and Well EngineeringFrench-language works237,207