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Record W2168675226 · doi:10.2118/89866-ms

A Comparative Study of Laboratory Techniques for Measuring Capillary Pressures in Tight Gas Sands

2004· article· en· W2168675226 on OpenAlexaff
K. E. Newsham, J. A. Rushing, Patrick M. Lasswell, J. C. Cox, T. A. Blasingame

Bibliographic record

VenueSPE Annual Technical Conference and Exhibition · 2004
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsApache (Canada)
Fundersnot available
KeywordsCapillary pressureCapillary actionCentrifugeSaturation (graph theory)Mercury (programming language)PorosityDesorptionVapor pressurePressure measurementMaterials sciencePorous mediumAnalytical Chemistry (journal)MechanicsPetroleum engineeringChemistryComposite materialGeologyChromatographyThermodynamics

Abstract

fetched live from OpenAlex

Abstract This paper presents results from a laboratory study comparing capillary pressure measurement techniques for tight gas sands. Included in our evaluation are the more traditional high-speed centrifuge and high-pressure mercury injection methods as well as the less conventional high-pressure porous plate and vapor desorption techniques. The results of our study show significant differences between the mercury injection data and composite capillary pressure curves constructed with data from the other three methods. Consequently, we have concluded that high-pressure mercury injection can be used to quantify pore size distribution, but often inaccurately characterizes capillary pressures, particularly at the irreducible water saturation. Moreover, our study suggests that a composite capillary pressure curve constructed from a combination of the vapor desorption data for the low water saturation range and high-speed centrifuge or high-pressure porous plate data for the high saturation range provides the most accurate capillary pressures for tight gas sands.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.293
Threshold uncertainty score0.454

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 head0.023
GPT teacher head0.270
Teacher spread0.247 · 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

Citations53
Published2004
Admission routes1
Has abstractyes

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