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Record W2935098193 · doi:10.11159/icmfht19.118

Towards Experimental Measurement of Methane Adsorption Isotherm in Shale Reservoirs

2019· article· en· W2935098193 on OpenAlexaff
Razieh Solatpour, Apostolos Kantzas

Bibliographic record

VenueProceedings of the World Congress on Momentum, Heat and Mass Transfer · 2019
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMethaneOil shalePetroleum engineeringAdsorptionShale gasSorption isothermEnvironmental scienceGeologyChemistry

Abstract

fetched live from OpenAlex

Unconventional petroleum resources, especially shales, constitute an increasing frontier of reserves additions as conventional production declines. This research is a study to investigate the interactions between shale rocks with gaseous hydrocarbons and mainly uses NMR logging which is a powerful tool to obtain in-situ rock and fluid properties of hydrocarbon reservoirs. In this study, a novel procedure is developed to quantitatively estimate the excess adsorption of gaseous hydrocarbon on shale gas reserves using direct measurements of an in-house built NMR setup. NMR experiments are conducted to measure NMR-porosity, relaxation distribution, and pore size distribution. These measurements are compared at different pressures in porous media with different adsorption capacity. As a result, adsorbed hydrocarbon gas content is estimated, and finally, adsorption isotherm is presented. Additional experiments such as gas expansion porosity and gravimetric adsorption are conducted to validate the new NMR method. In this research, for the first-time Low-Field NMR relaxometry with frequency close to logging tools is used for quantitative determination of adsorption isotherms of methane in shale reservoirs. These measurements help estimate the gas content and differentiate between adsorbed and free gas in porous media, paving the way for studies such as natural gas storage in shale rock, CO2 sequestration, and tight enhanced recoveries such as gas flooding and cyclic solvent injection.

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 score0.009
Threshold uncertainty score0.526

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 head0.015
GPT teacher head0.224
Teacher spread0.210 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

Explore more

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