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Record W2061209227 · doi:10.2118/170753-ms

Use of a Non-Invasive, Non-Damaging Blocking Agent for Improved Cleaning in Low Bottomhole Pressure Wells

2014· article· en· W2061209227 on OpenAlexaff
Ochiagha Victor Ananaba, Larry L. Hain, Jason A. White, Steven Craig, Richard T. Koch

Bibliographic record

VenueSPE Annual Technical Conference and Exhibition · 2014
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsConocoPhillips (Canada)
FundersConocoPhillips
KeywordsPetroleum engineeringLost circulationMaterials sciencePermeability (electromagnetism)Blocking (statistics)MicelleChemical engineeringChemistryGeologyEngineeringAqueous solutionComputer scienceMembraneMetallurgyDrilling fluid

Abstract

fetched live from OpenAlex

Abstract Solids removal from severely depleted wells remains a challenging intervention operation in the oil and gas industry. The various cleanout options that exist generally require significant expense, and may result in loss of potentially damaging fluids or permeability reduction to zones with low bottomhole pressures. This paper will focus on several field trials in South Texas utilizing a fluid-based, formation-blocking agent as part of the cleanout circulating fluid. The blocking agent has been in use for several years in cement spacer systems as a lost circulation material (LCM) with favorable results. The blocking agent uses a modified, hydrophobic polysaccharide to form micelles. Under differential pressure the micelles adsorb and realign in a layer along the formation or perforation tunnel forming an impermeable seal effectively blocking the loss of fluids to the formation. With the formation blocked, the solids can be circulated out using a higher density fluid and at higher circulation rates than the low-pressure reservoir would support. When the well is returned to an underbalanced system, the micelles will disperse, returning the fluid to its original state and restoring permeability to near a pre-intervention level. The case histories included in this paper are the first application of this material for well intervention operations outside a cement spacer application, and the results discussed show that the material performed remarkably well.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.013
GPT teacher head0.222
Teacher spread0.209 · 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 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

Citations3
Published2014
Admission routes1
Has abstractyes

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