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Record W2264019255 · doi:10.1144/0071141

Athabasca oil sands: reservoir characterization and its impact on thermal and mining opportunities

2010· article· en· W2264019255 on OpenAlexaffabout
M. J. Peacock

Bibliographic record

VenueGeological Society London Petroleum Geology Conference series · 2010
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsImperial Oil (Canada)
Fundersnot available
KeywordsOil sandsPetroleum engineeringReservoir modelingCharacterization (materials science)GeologyMining engineeringEnvironmental scienceGeochemistryAsphaltMaterials scienceArchaeologyGeography

Abstract

fetched live from OpenAlex

Abstract The heavy oil deposits of Canada are a large resource with an estimated 1.7×10 12 barrels of bitumen in place. The Lower Cretaceous McMurray Formation in the Athabasca area of northern Alberta contains about 900×10 9 barrels of bitumen in place. This resource can be developed through surface mining and thermal in situ techniques. This paper examines the size of this resource in a global context and highlights its position relative to the North American market. The regional geology of the Western Canada Sedimentary Basin and the Athabasca area will also be reviewed. Understanding the regional reservoir distribution of the McMurray Formation is critical to understanding oil sands opportunities. Fluvial estuarine point bar reservoirs are a large portion of the resource that can be developed. Examples will be shown from the type outcrop location, where the stratigraphy can be organized into a hierarchy that subdivides channel-fills into bedsets, storeys, bars and barsets. Inclined heterolithic strata (IHS) surfaces can be identified. Considerable resource delineation drilling has occurred in the basin. The regulator for the Athabasca area specifies minimum drilling densities before project approvals are granted. Regional 2D seismic lines and project-specific 3D and 4D seismic datasets have also been acquired, to reduce the reservoir uncertainty and improve resource definition in this complex depositional environment. These techniques provide a unique opportunity to analyse a complex depositional system with abundant well and core control, outcrop data and seismic information. To determine preliminary deposition environments, software techniques have been successfully used to interpret large datasets quickly. Laser imaging of mine faces has also been used to record stratigraphy and determine the mined volume of ore. The importance of detailed reservoir characterization studies and their impact on thermal in situ recovery mechanisms will also be discussed. Understanding reservoir facies distributions and lateral relationships affects any recovery process, but has an even greater significance in a heavy oil reservoir.

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 categoriesMeta-epidemiology (narrow)
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 score0.682
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.022
GPT teacher head0.235
Teacher spread0.213 · 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.

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

Citations36
Published2010
Admission routes2
Has abstractyes

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