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Record W2585745628 · doi:10.11575/prism/24868

A New Material Balance Methodology for Quintuple Porosity Shale Gas and Shale Condensate Reservoirs

2016· dissertation· en· W2585745628 on OpenAlexfundno aff
Orozco Ibarra

Bibliographic record

VenuePRISM (University of Calgary) · 2016
Typedissertation
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsnot available
FundersChina National Offshore Oil CorporationUniversity of Calgary
KeywordsOil shaleShale gasMaterial balancePetroleum engineeringPorosityOil shale gasBalance (ability)Shell in situ conversion processGeologyUnconventional oilGeotechnical engineeringEngineeringProcess engineeringPaleontology

Abstract

fetched live from OpenAlex

A recent petrophysical formulation states that all the storage mechanisms present in shale reservoirs are best represented by a quintuple porosity system that is further fed by dissolved gas in the solid kerogen. The quintuple porosity system is made up of: 1) adsorbed gas in the pore walls of the organic matter, 2) free gas stored in the inorganic matrix porosity, 3) free gas stored in natural fractures (microfractures and slot porosity), 4) free gas stored in the hydraulic fractures created around the wellbore by the stimulation job, and 5) free gas stored in the organic nanopores. This thesis presents a new material balance methodology for shale gas and shale condensate reservoirs that considers all the aforementioned storage mechanisms. Results lead to the conclusion that ignoring the effects of gas diffusion from kerogen in shale material balance calculations can lead to pessimistic estimates of both OGIP and production forecasts.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.232
Teacher spread0.212 · 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 designSimulation or modeling
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

Citations5
Published2016
Admission routes1
Has abstractyes

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