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Record W2340486262 · doi:10.2118/180110-ms

An Application of the Isogeometric Analysis Method to Reservoir Simulation

2016· article· en· W2340486262 on OpenAlexfundno aff
Eric Alexander Lynd, John T. Foster, Quoc P. Nguyen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Numerical Analysis Techniques
Canadian institutionsnot available
FundersCMG Reservoir Simulation FoundationUniversity of Texas at Austin
KeywordsIsogeometric analysisDiscretizationFinite element methodComputer scienceConvergence (economics)Applied mathematicsDegrees of freedom (physics and chemistry)Field (mathematics)Mathematical optimizationSource codeAlgorithmComputational scienceMathematicsMathematical analysisStructural engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

Abstract The isogeometric analysis method (IGA) has been an emerging technique for exact geometrical discretization and efficient high order approximations in the field of computational engineering. While its potential has been demonstrated in a variety of disciplines, it has yet to be applied extensively to the field of reservoir simulation. This work shows IGA's potential as a useful tool for next generation reservoir simulation, highlighting its ability to exactly capture complex-shaped geometrical features in the reservoir and, consequentially, accurately model the pressure fields. First, an IGA code is validated against the analytic solution for a quarter five-spot pattern as well as a reference solution for a straight line source problem with pressure enforced at the corners of the domain. Next, the numerical efficiency of IGA is compared against a finite element method solution of single phase flow in a 2D representative reservoir containing a centralized "S-shaped" line source. Results suggest a clear advantage of the IGA method over the classical finite element method, which has lower convergence rates in terms of number of degrees of freedom.

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 score0.826
Threshold uncertainty score0.226

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.005
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 head0.010
GPT teacher head0.328
Teacher spread0.318 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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