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Record W2011221338 · doi:10.2118/128926-ms

Improved Drilling Performance and Economics Using Hybrid Coiled Tubing Unit on the Chittim Ranch, West Texas

2010· article· en· W2011221338 on OpenAlexaff
B. Littleton, Sarah R. Nicholson, C. G. Blount

Bibliographic record

VenueIADC/SPE Drilling Conference and Exhibition · 2010
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsConocoPhillips (Canada)
FundersConocoPhillips
KeywordsDrillingCoringCasingDrillPetroleum engineeringDrilling rigEngineeringMeasurement while drillingMarine engineeringMechanical engineering

Abstract

fetched live from OpenAlex

Abstract A significant performance and cost improvement was achieved with the application of hybrid Coiled Tubing (CT) drilling equipment and techniques on the Chittim Ranch in Maverick county Texas. During this drilling program, 233 wells were grass-root drilled using the hybrid CT unit. Coiled tubing drilling equipment and techniques reduced the average time to complete a well by 60% when compared to conventional rotary rig drilling. This increase in drilling performance coupled with a turnkey contract resulted in a 14% (33% adjusted for inflation) cost reduction per well when compared to the most recent conventional drilling data from this area. This paper will review the process used in choosing a CT solution, the hurdles overcome, the problems encountered, and the lessons learned in managing and operating this CT Drilling (CTD) campaign. The paper will also provide an overview of CT coring performed in one of the wells during the CTD campaign. The drilling performance increase was realized using a top-set rig to set surface casing and a hybrid "Big Wheel" CT rig to drill the production hole. Finalized project data demonstrated that field performance using CTD met and in some areas exceeded project goals.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.298
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.015
GPT teacher head0.192
Teacher spread0.177 · 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.

Study designSimulation or modeling
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

Citations6
Published2010
Admission routes1
Has abstractyes

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