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Record W2749242532 · doi:10.1021/acs.iecr.7b02431

Laboratory-Scale Investigation of Sorption Kinetics of Methane/Ethane Mixtures in Shale

2017· article· en· W2749242532 on OpenAlexfundno aff
Devang Dasani, Yu Wang, Theodore T. Tsotsis, Kristian Jessen

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2017
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsnot available
FundersCMG Reservoir Simulation Foundation
KeywordsMethaneSorptionOil shaleDesorptionChemistryPropaneAdsorptionNatural gasChemical engineeringOrganic chemistryGeology

Abstract

fetched live from OpenAlex

Natural gas produced from shales is composed primarily of methane (CH 4 ), accounting for up to 87–96 mol %. In addition to CH 4, shale gas contains a host of secondary components, including nitrogen (N 2 ), helium (He), and hydrocarbons such as ethane (C 2 H 6 ) and propane (C 3 H 8 ). CH 4 and the other hydrocarbons are thought to be stored in the adsorbed state in the micropores and mesopores of the shale, and as free gas in the (natural) fracture networks. Although convective transport and diffusive transport account for the short-term behavior during shale gas production, desorption is thought to dominate the long-term dynamics of shale gas production. The key objective of this study, therefore, is to investigate the sorption kinetics of methane/ethane mixtures in gas shales. Specifically, we study here the adsorption/desorption behavior of pure CH 4 and C 2 H 6, and their mixtures in a whole shale sample (cube) using thermogravimetric analysis (TGA). The choice of ethane is because it is, typically, the second largest component of shale gas and is thought to compete for the same adsorption sites as methane. To this end, we first determine the steady-state isotherms of the pure component gases and their binary mixtures, which are essential to predicting the gas storage capacity of the shale. We then study the dynamics toward equilibrium during the sorption process in order to better understand the role of desorption during the later times of shale gas production. We apply the well-established Langmuir approach to analyze and interpret the experimental dynamic sorption observations. Our experimental data predict a lag in ethane production relative to that of methane due to the preferential sorption of ethane on the shale. This provides for added insight into interpreting field-scale production data in terms of the produced gas compositions. The experimental observations and their analysis pave, therefore, a path toward improving the interpretation of production data from shale gas operations via an enhanced understanding of desorption dynamics (and subsequent mass transfer) of gas mixtures in shale.

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 score0.005

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 integrity0.0000.001
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 head0.073
GPT teacher head0.314
Teacher spread0.241 · 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

Citations39
Published2017
Admission routes1
Has abstractyes

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