MétaCan
Menu
Back to cohort
Record W2019148525 · doi:10.2118/171633-ms

Petrophysical and Geomechanical Characteristics of Canadian Tight Oil and Liquid-Rich Gas Reservoirs

2014· article· en· W2019148525 on OpenAlexafffundabout
Amin Ghanizadeh, Sérgio Francisco de Aquino, Christopher R. Clarkson, Omid H. Ardakani, Hamed Sanei

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsGeological Survey of CanadaUniversity of Calgary
FundersSeven Generations Energy
KeywordsPetrophysicsPermeability (electromagnetism)PorosityGeologySiltstoneMineralogyGrain sizeOil sandsWell loggingGeotechnical engineeringComposite materialMaterials sciencePetroleum engineeringAsphaltGeomorphologyChemistry

Abstract

fetched live from OpenAlex

Abstract The results from an ongoing laboratory study investigating petrophysical and geomechanical characteristics of the Montney and Bakken formations in Canada are presented. The primary objectives are to 1) fully characterize the pore network (porosity, pore size distribution) and fluid transport (permeability) properties of these formations in areas with limited datasets; 2) investigate the interrelationship between petrophysical and geomechanical characteristics of these fine-grained tight reservoirs; and 3) analyze the effects of different geological factors on porosity, pore size distribution and permeability. The techniques used for characterization include: Rock-Eval pyrolysis (Tmax, TOC); bitumen reflectance; petrography (grain size); helium pycnometry; low-pressure gas (N2) adsorption (surface area, pore size distribution); pressure-decay profile permeability, pulse-decay and crushed-rock gas (N2, He) permeability; fracture permeability and mechanical hardness tests. Rock-Eval analysis and microscopic observations indicate that most samples are organic-lean (average TOC content: 0.3%), ranging from fine-grained siltstone to very fine-grained sandstone (grain size: 31.8-53.7 μm). The measured pulse-decay and crushed-rock permeability values increase significantly with increasing porosity (2.1-14.1%), ranging between 1.1·10-6 and 7.3·10-2 mD. For the plugs analyzed ("as-received"), profile (probe) permeability values (9.1·10-4 - 6.7·10-3 mD) are consistently higher than pulse-decay (1.6·10-5 - 9·10-4 mD) and crushed-rock (1.1·10-6 - 5.4·10-5 mD) permeability values. Corrected profile (probe) permeability values for "in-situ" effective stress (5.3·10-5 - 1·10-3 mD) are, however, comparable with the pulse-decay (1.6·10-5 - 9·10-4 mD) permeability values. Unpropped fracture permeability, determined using an innovative procedure in this work, can be significantly (up to eight orders of magnitude) higher than matrix permeability under similar effective stress conditions. The grain size and mechanical hardness data are correlated to permeability. The dominant pore throat diameter controlling fluid flow is estimated for all samples using Winland-style correlations; these values agree with those obtained from low-pressure N2 adsorption analysis. Applying multiple innovative analysis techniques on a large number of samples (26 m of slabbed core, 22 core plugs and their accompanying cuttings), this study provides a roadmap to fully characterize the fluid storage and transport properties of fine-grained tight oil and liquid-rich gas reservoirs. We demonstrate that pore structure, large- and fine-scale (cm-size) permeability heterogeneity, and mechanical characteristics of tight oil and liquid-rich gas reservoirs can be suitably-characterized using the methods we have used with application to flow-unit identification and mechanical stratigraphy determination. We further present useful correlations between petrophysical and geomechanical properties for the reservoirs studied.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.007
GPT teacher head0.186
Teacher spread0.179 · 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 designObservational
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

Citations26
Published2014
Admission routes3
Has abstractyes

Explore more

Same topicHydrocarbon exploration and reservoir analysisFrench-language works237,207