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Record W1986481938 · doi:10.2118/92195-ms

Casing Directional Drilling Using a Rotary Steerable System

2005· article· en· W1986481938 on OpenAlexaff
Robert Strickler, Todd Mushovic, Tommy Warren, Bill Lesso

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsConocoPhillips (Canada)
Fundersnot available
KeywordsCasingDrillingMeasurement while drillingPetroleum engineeringDirectional drillingRSSEngineeringSubmarine pipelineDrilling engineeringOffshore drillingMarine engineeringDrilling fluidMechanical engineeringGeologyGeotechnical engineeringComputer science

Abstract

fetched live from OpenAlex

Abstract The hardware used in casing while drilling operations over the past 5 years has proved to be robust and reliable. Several directional wells have been drilled successfully with casing using positive displacement motors (PDMs), but the drilling efficiency was significantly degraded in most of them. This paper documents the drilling of the first two wells to utilize a rotary steerable system (RSS) to improve drilling efficiency when drilling with casing. Combining an RSS with casing while drilling operations seemed to be a natural way to eliminate the major weaknesses in motor bottomhole assembly (BHA) designs. Rotary steerable systems had not previously been used with casing while drilling because both are new technologies focused on different environments. Casing while drilling developed on land operations while rotary steerables have been popular for offshore projects. ConocoPhillips has drilled more than 94 wells in the Lobo trend of South Texas since 2001 using the new technique. As part of a technology demonstration project to accelerate the technology to offshore applications, two wells were drilled with an RSS. The first was an operational test conducted by drilling vertically with the RSS. The second was a full directional test o with a build to 29° directional turn. and then a drop to vertical, including a 100°

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.734
Threshold uncertainty score0.655

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 head0.007
GPT teacher head0.172
Teacher spread0.165 · 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 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

Citations9
Published2005
Admission routes1
Has abstractyes

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