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Record W1981177795 · doi:10.2118/141596-ms

Domain Decomposition Methods Applied to Coupled Flow-Geomechanics Reservoir Simulation

2011· article· en· W1981177795 on OpenAlexaff
Horacio Flórez, Mary F. Wheeler, A. Rodríguez, Jorge E. Monteagudo

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsConocoPhillips (Canada)
FundersConocoPhillips
KeywordsDomain decomposition methodsGeomechanicsMortar methodsComputer scienceComputational scienceFinite element methodPolygon meshGeologyEngineeringStructural engineeringGeotechnical engineeringComputer graphics (images)

Abstract

fetched live from OpenAlex

Abstract In this work, well established Domain Decomposition techniques have been studied in order to carry out efficient simulations of coupled flow/geomechanics problems by taking full advantage of current parallel computer architectures. Different solution schemes can be defined depending upon transmission conditions among sub-domain interfaces. Three different schemes, i.e. Dirichlet-Neumann, Neumann-Neumann and Mortar-FEM, are tested and the advantages and disadvantages of each of them identified. This work will focus in the coupling of different meshes and/or physics on different domains by means of the Mortar-FEM plus the above DD-Schemes. Several examples of coupling of elasticity and poroelasticity in the context of reservoir compaction and subsidence are presented. In order to facilitate the implementation of complex workflows, we have implemented an advanced Python wrapper interface that allows programming capabilities. We have applied this platform to a variety of problems ranging from near-wellbore applications to field level subsidence calculations.

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: Methods · Consensus signal: none
Teacher disagreement score0.534
Threshold uncertainty score0.597

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 head0.025
GPT teacher head0.283
Teacher spread0.258 · 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
GenreMethods

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

Citations27
Published2011
Admission routes1
Has abstractyes

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