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Record W1992096387 · doi:10.2118/98440-ms

Simultaneous Dynamic Killing and Cementing of a Live Well

2006· article· en· W1992096387 on OpenAlexaff
Robert Strickler, D. J. Moore, Pablo Solano

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsConocoPhillips (Canada)
Fundersnot available
KeywordsComputer sciencePetroleum engineeringGeology

Abstract

fetched live from OpenAlex

Abstract Drilling with casing is a method of drilling wells using casing as the drillstring. This method has been used in South Texas to reduce well costs. To help reduce well costs even further, underbalanced drilling seemed to have the potential of enabling the elimination of an intermediate casing string. By drilling underbalanced, it would be possible to drill with a mud weight low enough to prevent loss of circulation in the shallow weak zones while dealing with the gas from the pay zones below. Drilling with casing eliminates the necessity of trips, thus simplifying underbalanced operations. However, once total depth (TD) was reached and drilling complete, it would be necessary to balance the well to allow cementing of the production casing string. The challenge was to develop a way to kill the well so that the cement could be placed and allowed to set while at the same time not breaking down and losing circulation into the weak upper zones. This had to be done by having different equivalent circulating densities (ECD) and equivalent mud weights (EMW) up and down the hole throughout the circulating and cementing operation. In addition, this technique needed to be relatively simple and easily accomplished in the field. All this had to be done without shutting in the well or using chokes, since that would also cause breaking down the upper zone and result in an underground blowout. This paper describes the development of the required methodology and its successful field application in South Texas. This same technique can readily be applied elsewhere.

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: Empirical
Teacher disagreement score0.200
Threshold uncertainty score0.358

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 head0.002
GPT teacher head0.160
Teacher spread0.158 · 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
Published2006
Admission routes1
Has abstractyes

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