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Record W3033882098 · doi:10.1002/9781119593324.ch17

Injection of Non‐Condensable Gas in SAGD Using Modified Well Configurations ‐ A Simulation Study

2020· other· en· W3033882098 on OpenAlexaff
Yushuo Zhang, Brij Maini

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPetroleum engineeringComputer simulationInjectorEnvironmental scienceMaterials sciencePulp and paper industrySimulationComputer scienceGeologyMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

The objective of this research is to examine the feasibility of NCG co-injection with steam in SAGD using modified well configurations using numerical simulation. The NCG used in this project is methane. The aim is to form a more stable insulating layer just below the top of formation, which results in lowered overburden heat loss and cSOR. To place the NCG directly below the overburden rock, vertical steam injectors with dual completions are implemented in this study. Simulation runs are created using CMG STARS (Thermal and Advanced Process Simulator). These simulation results demonstrated substantial improvement in cSOR by injecting NCG separately from steam using the dual completed vertical injectors. The producer operational constraint is a combination of maximum live steam rate and minimum bottom-hole pressure (BHP). The base case covers production from May 2011 to January 2020 in steam only SAGD operation. The base case of non-flowing boundary condition showed approximately 157,000 m3 oil production in about 10 years, with a cumulative Steam Oil Ratio (cSOR) of 9.0. The simulation results show that injecting the non-condensable gas improved cSOR but maintained similar cumulative oil production compared to conventional SAGD process. As expected, non-condensable gas reduces the gas mobility and stabilizes the insulating blanket formed by high gas saturation at the top of the steam chamber. The optimized simulation case with three vertical wells enabled the cSOR to be as low as 2.27.

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 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: none
Teacher disagreement score0.938
Threshold uncertainty score0.998

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 integrity0.0000.000
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 head0.041
GPT teacher head0.314
Teacher spread0.273 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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