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Record W2795180422 · doi:10.2118/183721-pa

Senlac, the Forgotten SAGD Project

2018· article· en· W2795180422 on OpenAlexaffabout
Eric Delamaide

Bibliographic record

VenueSPE Reservoir Evaluation & Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsSteam-assisted gravity drainageOil sandsPetroleum engineeringSteam injectionOil fieldOil productionEngineeringGeologyAsphaltArchaeologyGeography

Abstract

fetched live from OpenAlex

Summary The Senlac steam-assisted-gravity-drainage (SAGD) project in Saskatchewan, Canada, does not have the same name recognition as its much bigger brothers in the Alberta Oil Sands, but it certainly deserves to be known better. Senlac was the first industrial SAGD project in Canada, back in 1997, and since then, it has been the site for other technological innovations such as the use of solvent in addition with steam to increase recovery and reduce the steam/oil ratio (SOR), as well as the testing of wedge wells—wells drilled between SAGD well pairs to benefit from the heat remaining in the reservoir. The reservoir in Senlac is the Dina-Cummings of the Lower Cretaceous, and is much smaller than the McMurray formation, the site of most large-scale oil-sands projects, but the oil is only 5,000 cp; thus, it is mobile at reservoir temperature. This is a significant difference that allows well pairs to achieve excellent production and recovery even though reservoir thickness is only 8 to 16 m, well below the standard cutoff for SAGD. The presence of bottomwater under parts of the field is an added challenge to the operations. The paper will present the field characteristics and production performances as well as the main technological developments such as the solvent-added process (SAP) and the use of wedge wells. The paper will present a complete case study of an SAGD project in a heavy-oil reservoir where oil is mobile. Most SAGD projects so far have been conducted in bitumen, but the paper will show the potential for this technology in thinner and smaller reservoirs.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.147
Threshold uncertainty score0.783

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.025
GPT teacher head0.282
Teacher spread0.256 · 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.

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

Citations9
Published2018
Admission routes2
Has abstractyes

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