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Record W1977912641 · doi:10.2118/133801-ms

Operator/Service Company Research Collaboration Leads to Reduced Downhole Vibration Levels and Improved Drilling Performance

2010· article· en· W1977912641 on OpenAlexaboutno aff
Steven W. Chesher, Mark E. Williamson, Bill B. Dougherty, Reed Spencer, Kurtis Schmitz, Sarvesh Tyagi

Bibliographic record

VenueSPE Annual Technical Conference and Exhibition · 2010
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
FundersDevon Energy Corporation
KeywordsDirectional drillingDrillingPetroleum engineeringEngineeringWorkoverDrilling fluidOil shaleDrilling engineeringMechanical engineeringDrill bit

Abstract

fetched live from OpenAlex

Abstract The Cana Woodford shale play is emerging as an active natural gas field in Oklahoma, USA with over 100 wells already drilled and many more in the pipeline. However, the key to success, especially with the current depressed pricing of natural gas, is continued injection of technology leading to reduced drilling costs. The most common well profile drilled in Canadian County and Blaine County, Oklahoma consists of a vertical section followed by a curve section and a long horizontal section through the Cana Woodford shale. Downhole vibrations were identified as major drilling performance limiters in the 8 ¾ in. and 12 ¼ in. vertical hole sections in these applications. The operator and the service company started a collaborative project to measure, understand, and reduce vibrations of the bit and BHA in order to improve drilling performance. The project adopted a holistic approach that included the use of analytical drilling models, drilling lab facilities, an experimental test rig and field testing to study how bits, BHA's and operating parameters impact downhole vibrations. All testing in the drilling lab, at the experimental test rig, and in the field incorporated in-bit vibration sensors. In this paper, the authors will illustrate how a collaborative effort between an operator and a service company can leverage the collective knowledge and experience of both organizations for scientific investigation into the most important drilling dysfunction -downhole vibration. Based on testing in the lab, the experimental rig and the field, changes in the BHA and drive mechanism were implemented. These changes led to significant reductions in vibration levels, improved performance and better bit dull conditions. It is anticipated that as the collaboration moves forward, reductions in downhole vibration will translate into continued improvement in ROP, longer tool life, and significant cost savings for the operator.

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)0.010
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0250.004

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 head0.029
GPT teacher head0.293
Teacher spread0.264 · 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 designObservational
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
Published2010
Admission routes1
Has abstractyes

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