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Record W2058823901 · doi:10.2118/144543-ms

Numerical Optimization of Clearwater Formation's Response to SAGD under New Well Configurations

2011· article· en· W2058823901 on OpenAlexaffabout
M. Sadegh Tavallali, Brij Maini, T. Harding

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSteam-assisted gravity drainagePetroleum engineeringOil sandsInjectorWellboreAsphaltSteam injectionEnvironmental scienceOil fieldGeologyEngineeringMechanical engineeringMaterials science

Abstract

fetched live from OpenAlex

Abstract The Cold-Lake oil sands contain the second largest reserves volumes among the oil sands deposits in Canada. The bitumen and heavy oil are contained in various sands of the Lower Cretaceous Mannville Group – Clearwater Formation. For the past 30 years, Cyclic Steam Stimulation (CSS) has been the commercial thermal recovery method employed in the Cold Lake area. More recently, Steam-Assisted Gravity Drainage (SAGD) has been field tested in number of pilot projects at Cold Lake. Although SAGD has been demonstrated to be technically successful and economically viable, questions remain regarding SAGD performance compared to CSS. A more comprehensive understanding of the parameters affecting SAGD performance in the Cold Lake area is required. Well configuration is one of the major factors which require greater consideration for process optimization. This paper presents a numerical simulation investigation of the impact of using several modified well configurations for SAGD in the Clearwater Formation in Cold Lake area in order to improve the process performance. The technical feasibility of applying each arrangement was evaluated through sensitivity analysis using a fully implicit reservoir thermal simulator (CMG STARS 2009.13). In order to account for frictional pressure drop and heat losses along the wellbore, the fully coupled wellbore/reservoir (discretized wellbore) model was utilized during the course of this study. The reservoir and fluid properties were selected to represent the main bitumen production area at Cold Lake. The new well configurations provide operational and economical enhancement to the SAGD process over the standard well configuration (a horizontal injector lying approximately 5 meters above a horizontal producer) in Cold Lake area. The SAGD process response to different reservoir parameters of the Cold Lake Formation, such as initial injectivity, mobile water saturation, and reservoir heterogeneity has been investigated for the most promising of the new well configurations.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.018
GPT teacher head0.223
Teacher spread0.204 · 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

Citations1
Published2011
Admission routes2
Has abstractyes

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