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Record W4392236698 · doi:10.2118/217951-ms

Mechanistic Study and Chemical Design of a New Lubricant for High Salinity Drilling Fluids

2024· article· en· W4392236698 on OpenAlexaffabout
J. Brockhoff, Melinda E. Taylor, S. R. Dubberley, K. Ma, C. Jaska

Bibliographic record

VenueIADC/SPE International Drilling Conference and Exhibition · 2024
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsAlberta Energy
Fundersnot available
KeywordsLubricantDrilling fluidPetroleum engineeringSalinityDrillingGeologyEnvironmental scienceMaterials scienceMetallurgyComposite materialOceanography

Abstract

fetched live from OpenAlex

Abstract The drive to reduce freshwater consumption has led to a significant increase in the use of waste streams, such as high salinity produced water brines, as base fluids in drilling fluid systems. However, the performance of conventional lubricants is typically reduced in such brines. This paper describes the design and development of a new class of lubricant specifically designed for use in high salinity water-based drilling fluids. A comprehensive literature review and a detailed laboratory testing program were undertaken to investigate the mechanism of drilling fluid lubricants under downhole conditions. The knowledge gained from this study was used to design a new class of lubricant that undergoes a chemical reaction with high salinity brines in order to activate the lubricant species and maximize performance. A series of field trials were conducted on wells targeting the Montney formation in Western Canada. The drilling fluids consisted of solids-free sodium chloride, calcium chloride, and produced water brines with densities ranging from 1080 – 1330 kg/m3. The wells had lateral lengths ranging from 1580 – 4000 meters. The new lubricant significantly out-performed conventional lubricants on 100% of the field trials and was able to achieve friction factors close to those of oil-based drilling fluids. A series of in-depth case studies are provided which highlight the unique performance characteristics of this novel drilling fluid lubricant.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.789
Threshold uncertainty score0.726

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 head0.029
GPT teacher head0.252
Teacher spread0.222 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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