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Record W2027915957 · doi:10.2118/165487-ms

A New Approach for Designing Steam Splitters and Inflow Control Devices in Steam Assisted Gravity Drainage

2013· article· en· W2027915957 on OpenAlexaff
Mohammad Kyanpour, Zhangxin Chen

Bibliographic record

VenueSPE Heavy Oil Conference-Canada · 2013
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsUniversity of Calgary
FundersGuangdong Academy of Sciences
KeywordsInjectorInflowSteam-assisted gravity drainagePetroleum engineeringSteam drumSteam injectionEngineeringAsphaltEnvironmental scienceOil sandsSuperheated steamMechanical engineeringBoiler (water heating)Waste managementGeologyMaterials science

Abstract

fetched live from OpenAlex

Abstract The classical SAGD involves drilling wells in parallel horizontal pairs. Steam is injected into the upper well (injector) to heat the reservoir and mobilize bitumen so that it drains to the lower well (producer) and can be lifted to the surface. In this process, steam distribution in the injector and a sustainable liquid level above the producer are key to achieve steam chamber conformance. The completion designs of these wells are critical in order to achieve optimal bitumen recovery and steam chamber development. Two common tools in SAGD wellbore completions are Steam Splitters and Inflow Control Devices. The Steam splitters are used to customize steam distribution in the injector. The Inflow Control Devices are used in the producer to develop a uniform inflow along the horizontal wellbore. This paper presents a method for determining the size and position of Steam Splitters and Inflow Control Devices. This method can be used for both simple and complex reservoirs containing heterogeneous geology and hydraulic barriers and baffles.

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: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.001
Research integrity0.0000.000
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 head0.023
GPT teacher head0.232
Teacher spread0.209 · 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

Citations15
Published2013
Admission routes1
Has abstractyes

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