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Effects of Dissolved Oxygen on Water Imbibition in Gas Shales

2018· article· en· W2793662803 on OpenAlexafffund
Mingxiang Xu, Mojtaba Binazadeh, Ashkan Zolfaghari, Hassan Dehghanpour

Bibliographic record

VenueEnergy & Fuels · 2018
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsImbibitionPyriteDissolutionOil shaleChemistryOxygenEnvironmental chemistryHydraulic fracturingSulfateSalinityInorganic chemistryMineralogyGeologyPetroleum engineering

Abstract

fetched live from OpenAlex

Understanding the water uptake of gas shales is critical for designing and optimizing hydraulic fracturing operations during which a large volume of fracturing water containing dissolved oxygen is injected into tight reservoirs. Recent studies show that the dissolved oxygen may promote oxidation reactions which can affect salinity and pH value of flowback water; however, the effects of dissolved oxygen and oxidation reactions on water imbibition into the shale matrix and on the concentration of individual ions in flowback water are still poorly understood. In this study, we conduct water imbibition experiments under degassed and oxic conditions, and measure the imbibed water mass and concentrations of different ions in water. The results show that the initial rate and final amount of water imbibition are higher under degassed conditions compared with that under oxic conditions. These differences are mainly due to the enhanced dissolution of air in the shale pore network into the imbibing water under degassed conditions and the consequent increase in relative permeability of water. The results also suggest that oxidation of pyrite by dissolved oxygen produces sulfate and iron ions. The abundance of pores in the vicinity of pyrite minerals as observed in the SEM/EDS images provides a pyrite, water, and oxygen rich environment which favors pyrite oxidation.

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 score0.056
Threshold uncertainty score0.302

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 head0.004
GPT teacher head0.193
Teacher spread0.189 · 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

Citations32
Published2018
Admission routes2
Has abstractyes

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