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Record W2051859903 · doi:10.2118/78192-ms

New Iterative Coupling between a Reservoir Simulator and a Geomechanics Module

2002· article· en· W2051859903 on OpenAlexaff
David Tran, A. Settari, Long D. Nghiem

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsGeomechanicsCoupling (piping)Modular designConvergence (economics)Computer scienceIterative methodFlow (mathematics)Stress (linguistics)Computational scienceAlgorithmMechanicsMechanical engineeringEngineeringGeotechnical engineeringPhysics

Abstract

fetched live from OpenAlex

Abstract Interactions of solid mechanics and fluid flow have been studied by numerous researchers for the past several years. Different methods of coupling such as full coupling, iterative coupling, etc., have been used. Nevertheless, the accuracy and the large run time of the coupled solid-mechanics fluid-flow model are outstanding issues that prevent the application of the coupled model in full-field studies. In this work, a novel relationship of porosity as a function of pressure, temperature and mean total stress is developed for iterative coupling of stress and flow. The new formula not only improves the accuracy of the coupling, but also reduces substantially the number of coupling iterations. The latter feature decreases significantly the CPU time. The new approach was implemented in a modular, iteratively coupled system. The rapid convergence provides the equivalent of a fully coupled method that is necessary to investigate complex coupled problems. The main advantage of this type of coupling is that a geomechanics module can be easily coupled with different reservoir simulators. The paper gives some comparisons of results obtained by the new porosity formula with another formulation.

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 score0.373
Threshold uncertainty score0.554

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)0.0010.000

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 head0.016
GPT teacher head0.221
Teacher spread0.205 · 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

Citations56
Published2002
Admission routes1
Has abstractyes

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