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Record W2326906837 · doi:10.2118/175954-ms

Impact of Solvent-Extraction on Fluid Storage and Transport Properties of Montney Formation

2015· article· en· W2326906837 on OpenAlexafffundabout
Amin Ghanizadeh, Christopher R. Clarkson, Sérgio Francisco de Aquino, A. Vahedian, Omid H. Ardakani, Hamed Sanei, James M. Wood

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsGeological Survey of CanadaEncana (Canada)University of Calgary
FundersSeven Generations Energy
KeywordsPetrophysicsSolventToluenePorosityExtraction (chemistry)AdsorptionGas pycnometerPermeability (electromagnetism)Materials scienceMineralogyChromatographyChemical engineeringGeologyChemistryComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Pore and pore-throat blocking materials may have a negative effect on reservoir quality, as has been recently determined for the low-permeability, hydrocarbon bearing portions of the Montney Formation. Some of these materials, such as salt and bitumen, may be extractable using different solvents combined with the Dean-Stark extraction process. The primary objective of the current study is therefore to investigate the impact of solvent-extraction on various geochemical and petrophysical characteristics of low-permeability intervals. To accomplish this goal, a comprehensive dataset was collected for two sample suites from the Montney Formation (western Alberta, northeastern British Columbia; Canada), before and after sequential solvent-extractions with organic solvents. The samples are analyzed after three different treatments: 1) "as-received", 2) toluene-extracted and dried, and, 3) toluene/methanol-extracted and dried. The methods used for characterization of the samples after each treatment are Rock-Eval pyrolysis (TOC content, S1, S2); helium pycnometry (grain density, porosity); low-pressure gas (N2, CO2) adsorption (surface area, pore volume, pore size distribution); and crushed-rock gas (He) permeability. Importantly, to ensure a proper comparison of the different sample treatments, the solvent-extraction and subsequent geochemical and petrophysical analyses are performed on identical samples; therefore, the effect of sample heterogeneity is mitigated. The impact of solvent-extraction on grain density, pore network attributes (surface area, pore volume, pore size distribution) and permeability of the Montney samples depends on the organic matter content, solvent type and other sample-to-sample variations. For one dataset (batch A), the change in petrophysical properties is variable and not predictable, while for the other (batch B), grain density, pore network attributes (surface area, pore volume, modal pore size distribution) and permeability exhibit an increase after sequential solvent-extraction with toluene and methanol. The variability observed for batch A is possibly attributed to (1) different degrees of salt precipitation, depending on the "in-situ" water/brine content and the salinity of the "in-situ" (formation) water and/or (2) experimental uncertainties/errors. A detailed discussion of the experimental uncertainties/errors is provided to elucidate the impact of these factors on the experimental outcomes. In the current study, it is demonstrated that by applying multiple analysis techniques on two diverse sample suites subject to three different treatments, the variation in pore structure and fluid flow characteristics of fine-grained tight oil/gas reservoirs before and after solvent-extraction can be quantified. The quantification of these effects may have important implications for both shale matrix transport characterization, which usually involves some form of extraction prior to petrophysical evaluation, and stimulation treatments for improving hydrocarbon recovery by removal of pore-blocking materials.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.288
Threshold uncertainty score0.573

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.033
GPT teacher head0.248
Teacher spread0.215 · 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

Citations13
Published2015
Admission routes3
Has abstractyes

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