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Record W2969888551 · doi:10.3390/en12173237

A Workflow for Optimization of Flow Control Devices in SAGD

2019· article· en· W2969888551 on OpenAlexafffund
Anas Sidahmed, Siavash Nejadi, Alireza Nouri

Bibliographic record

VenueEnergies · 2019
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsUniversity of CalgaryUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaRGL Reservoir Management
KeywordsPetroleum engineeringSteam-assisted gravity drainageSteam injectionCompletion (oil and gas wells)WorkflowOil sandsWellboreEngineeringDual (grammatical number)Enhanced oil recoveryAsphaltComputer scienceMaterials science

Abstract

fetched live from OpenAlex

In McMurray Formation, steam assisted gravity drainage is used as the primary in-situ recovery technique to recover oil sands. Different geological reservoir settings and long horizontal wells impose limitations and operational challenges on the implementation of steam-assisted gravity drainage (SAGD). The dual-string tubing system is the conventional completion scheme in SAGD. In complex reservoirs where dual-string completion cannot improve the operation performance, operators have adopted flow control devices (FCDs) to improve project economics. FCDs secure more injection/production points along the horizontal sections of the SAGD well pairs, hence, they maximize ultimate bitumen recovery and minimize cumulative steam-oil ratio (cSOR). This paper will focus on the optimization of outflow control devices (OCDs) in SAGD reservoirs with horizontal wellbore undulations. We present the detailed optimization workflow and show the optimization results for various scenarios with well pair trajectory undulation. Comparing the results of the optimized OCDs case with a dual-string case of the same SAGD model shows improvements in steam distribution, steam chamber growth, bitumen production, and net present value (NPV).

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 score0.338
Threshold uncertainty score0.252

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)0.0000.000

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 head0.008
GPT teacher head0.235
Teacher spread0.227 · 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

Citations6
Published2019
Admission routes2
Has abstractyes

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