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Record W2316164623 · doi:10.2118/174502-ms

Detailed History Matching of a SAGD Well Pair Using Discretized Wellbore Modeling

2015· article· en· W2316164623 on OpenAlexaboutno aff
K. N. Nguyen, L.T. Doan, K Kato

Bibliographic record

VenueSPE Canada Heavy Oil Technical Conference · 2015
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsWellheadPetroleum engineeringWellboreDiscretizationInjectorFluid dynamicsHydraulicsArtificial liftMechanicsGeologyEngineeringMechanical engineeringMathematics

Abstract

fetched live from OpenAlex

Abstract Circulation of steam during the start-up of a well pair is required for the SAGD process where there is limited fluid mobility, as it helps establish communication for flow between the injector and producer well. The majority of the modeling encountered to date for the circulation period either assumed a source/sink constraint, used a horizontal discretized wellbore model with sand face coupling, or used a pseudo-steady state wellbore flow model only. The above methodologies do not take into account 1) the heat transfers and pressure behavior among the different tubing strings, 2) the change in conditions from sand face to wellhead, and 3) the fluid interaction between the wellbore and reservoir. Using a thermally-coupled wellbore-reservoir simulator, a discretized wellbore model was constructed to allow a detailed history matching for the circulation period of the M-well pair at the Hangingstone Demo pilot. The history-matched circulation period helped better understand the effects of hydraulics, heat transfers between tubing strings and the impact of fluid losses on reservoir heating. This knowledge will help Japan Canada Oil Sands Limited (JACOS) design the operating philosophy for the Hangingstone Expansion wells. The paper will discuss how the matching was achieved along with the observations made.

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: Empirical
Teacher disagreement score0.422
Threshold uncertainty score0.960

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 head0.062
GPT teacher head0.262
Teacher spread0.200 · 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

Citations5
Published2015
Admission routes1
Has abstractyes

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