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Record W3009830782 · doi:10.11575/prism/37589

Modeling of Lithological Heterogeneity in a Heavy Oil Reservoir, Long Lake Property in the Athabasca Oil Sand Region, Northeastern Alberta

2020· dissertation· en· W3009830782 on OpenAlexaboutno aff
Md. Latif Ibna-Hamid

Bibliographic record

VenuePRISM (University of Calgary) · 2020
Typedissertation
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsnot available
Fundersnot available
KeywordsOil sandsGeologyGeochemistryPetroleum engineeringMining engineeringHydrology (agriculture)Environmental scienceArchaeologyGeotechnical engineeringGeographyAsphalt

Abstract

fetched live from OpenAlex

The objective of this research was to characterize lithological heterogeneity of the McMurray Formation, which is highly complex lithologically, both laterally and vertically. This study effectively characterizes the complete sedimentary succession of the McMurray Formation with physical and textural properties of rock. The physical properties are represented by specific surface area (SpSA) and dominant grain size (GS) and textural properties are characterized by tortuosity (τ/T), cementation exponent (m), tortuosity exponent (a) and pore aspect ratio (α). All these properties are derived by modifying empirical equation and presented in the form of continuous log, which is completely a new approach in presenting these data for the entire sedimentary succession. As all these parameters are derived using the conventional log data, therefore, there vertical resolution is similar to the resolution of conventional log, which is very important in generating high resolution 3D geo-cellular model. The continuous form of these data enables us to create 3D models, which facilitate to study their spatial distribution in a larger dimension, provide competency in cross-examining the related properties and can help to mitigate various heterogeneity related operational/production problems within the McMurray Formation. Application of these parameters in various permeability models enable to calculate permeability for the lithology ranging from unconsolidated sand to claystone/mudstone with honouring textural properties of rock. Successful application of ‘m’ and ‘a’ parameters in the permeability models validate the concept that ‘m’ and ‘a’ are not fixed for any particular lithology. The establishment of this concept is a significant outcome of this research, which indicates a new way of their application in petrophysics. Similarly, permeability results with the incorporated ‘α’ parameter in the Kozeny-Carman equation reveal that ‘α’ is a very strong textural parameter for permeability, and it appears that ‘α’ can be taken as a part of the coefficient of Kozeny-Carman equation.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.254
Threshold uncertainty score0.512

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 studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.020
GPT teacher head0.210
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 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
GenreOther

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

Citations0
Published2020
Admission routes1
Has abstractyes

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