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Record W1967790708 · doi:10.2118/68364-ms

Optimizing Acid Treatments With the Use of Jet Blasting Tool

2001· article· en· W1967790708 on OpenAlexaff
Ahmed Dahroug, Barry Brown

Bibliographic record

VenueSPE/ICoTA Coiled Tubing Roundtable · 2001
Typearticle
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsSchlumberger (Canada)
Fundersnot available
KeywordsWellheadNozzlePetroleum engineeringWellboreJet (fluid)Body orificeMechanical engineeringDrilling fluidMaterials scienceEngineeringDrillingAerospace engineering

Abstract

fetched live from OpenAlex

Abstract Coiled Tubing is a common method of diverting treatment fluids into wellbore formations in open hole and cased hole completions. The efficiency of the treatment can be significantly improved by increasing the impact force of the fluid on the formation. The recently developed JetBlaster™ Tool has been used on some cases as the optimum BHA that can be used with Coiled Tubing for the efficient delivery of treatment fluids. The software JetADVISOR supports system hydraulic analysis and the design of the tool configuration in terms of nozzle and orifice diameter. This allows optimization of jet impact force or hydraulic horsepower, and ensures a sufficiently high rotational speed to effectively divert the treatment around the circumference of the wellbore. The addition of velocity increases the affectivity of the treatment. In order to assist evaluation of the efficiency of the technique we have primarily attempted to treat a well that had formation drilling damage with the tool while pumping a non corrosive fluid that has no direct chemical reaction with the formation filter cake. The well was then put on the test to see the increase in production and wellhead pressure and the results recorded and compared. Then the same treatment was repeated but while jetting acid, then the well was once again put on test.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.681
Threshold uncertainty score0.705

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 communication0.0000.001
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 head0.030
GPT teacher head0.220
Teacher spread0.189 · 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 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

Citations4
Published2001
Admission routes1
Has abstractyes

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