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Record W2073586257 · doi:10.2118/07-06-02

Investigation of Key Parameters in SAGD Wellbore Design and Operation

2007· article· en· W2073586257 on OpenAlexaboutno aff
P.A. Vander Valk, Peter Yang

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2007
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsWellborePetroleum engineeringSteam-assisted gravity drainageArtificial liftEngineeringSteam injectionOil sandsMaterials science

Abstract

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Abstract A simulation study was carried out to evaluate the impact of wellbore pressure drops and subcool control on SAGD reservoir performance using a three-dimensional fully coupled reservoir/wellbore model. This work builds on several concepts introduced by others and attempts to provide a method to evaluate these concepts and the relationships between them. The results indicated a significant impact on steam chamber conformance, productivity and SOR at various operating pressures due to wellbore pressure drops. Pressure profiles within the injection and production wells were transferred into the reservoir, skewing the predominantly gravity drainage process. The chamber shape and conformance influence performance as it relates to subcool control. Sensitivities were run to evaluate the impact of key parameters. This paper highlights some critical factors that affect SAGD performance and behaviour. Potential mitigating measures are introduced, such as liner and tubing design, variable perforations and injection ports, along with practical operating implications of each. The impacts of the findings on artificial lift selection and operation are discussed with regard to subcool control and net positive suction head available (HPSH). Potential implications to low pressure (LP-SAGD) operations are also considered in that light. Introduction It is generally accepted that optimal SAGD performance requires control of produced fluids to some subcool value so that a liquid level is maintained above the production well. This liquid level reduces the tendency for steam to flow directly into the production well liner, which ensures that steam is used efficiently in the SAGD process. However, too much liquid accumulation can reduce productivity, primarily due to a lower fluid temperature and corresponding higher fluid viscosity at the drainage faces and at the critical points of convergence to the production liner. Optimum performance of the SAGD process can be defined for each project and reservoir type, however optimization is generally based on bitumen production rate (CDOR) and steam-to-oil ratio (SOR), often with conflicting imperatives. One critical parameter in this optimization is subcool. Nasr's work indicates that there is no appreciable difference in CDOR between 5 ° and 30 °C of subcool for two-dimensional models (Tawfik Nasr, Alberta Research Council, Personal Communication, February 2005). However, Ito and Suzuki(1) demonstrated that the optimum SOR for the Hangingstone reservoir occurs at subcool ranges of between 30 ° and 40 °C with reasonable productivity. Edmunds(2) indicated that 20 ° to 30 °C subcool was a reasonable operating target for the 2D cases. However, he discussed the real world complications of the three-dimensional case in some detail as well. Edmunds' work hinted that localized variability in subcool would occur, so that control of production rates to some optimum mixed or average subcool would result in steam production at some points along the liner, and large liquid level accumulations at other points. This is primarily due to the fact that the flow capacity of the wellbores is much greater than that of the reservoir in the same direction, making compensating steam movements in the reservoir difficult. Also, local liquid levels cannot effectively drain parallel to the well due to the very low drainage angles(2).

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: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
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 head0.018
GPT teacher head0.226
Teacher spread0.208 · 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

Citations33
Published2007
Admission routes1
Has abstractyes

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