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Record W2610145438 · doi:10.4043/27913-ms

Hydrate Management for Systems with High Salinity Brines at Ultra-High Pressures

2017· article· en· W2610145438 on OpenAlexaff
Amadeu K. Sum, Yue Hu, Bo Ram Lee, Prasad U. Karanjkar, Joseph Gomes, Greg Kusinski

Bibliographic record

VenueOffshore Technology Conference · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsConocoPhillips (Canada)
Fundersnot available
KeywordsHydrateSalinitySaturation (graph theory)Clathrate hydratePetroleum engineeringFlow assuranceGeologyEnvironmental scienceChemistry

Abstract

fetched live from OpenAlex

Reliable prediction of hydrate phase equilibrium is necessary for operations in deepwater oil and gas production which go beyond hydrate management in pipe flow, such as, setting depth of surface-controlled subsurface safety valve (SCSSV) and identification of effective risk mitigation. There are a number of fields being developed and producing with brines with salt concentration near/at saturation, creating conditions where hydrate formation may lead to salt precipitation. High salinity brines coupled with high pressures (> 10,000 psia) are conditions for which no hydrate phase equilibrium data exist in the open literature. This study, funded by DeepStar®, quantified the impact of high salinity brines in the formation of hydrates by the measurements of hydrate stability conditions in saline systems with under-saturated to saturated concentration through newly designed apparatus and development of correlation based on hydrate suppression temperature,providing reliable prediction results for hydrate phase equilibria in both NaCl and CaCl2 systems up to near-saturated concentrations.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.249
Threshold uncertainty score0.806

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.015
GPT teacher head0.231
Teacher spread0.216 · 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 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

Citations1
Published2017
Admission routes1
Has abstractyes

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