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Record W4285128186 · doi:10.3997/2214-4609.202210149

Fast Assessment of the Impact of Multi-Scale Geological Heterogeneities on Flow Behaviour in Complex Carbonate Reservoirs

2022· article· en· W4285128186 on OpenAlexafffund
J. Li, S. Geiger, J. Costa Gomes, D. Petrovskyy, Carl Jacquemyn, Gary J. Hampson, Matthew D. Jackson, J. Machado Silva, Sicilia Ferreira Judice, Fazilatur Rahman, Mário Costa Sousa

Bibliographic record

Venue83rd EAGE Annual Conference & Exhibition · 2022
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsUniversity of Calgary
FundersPetrobrasEnergi Simulation
KeywordsWorkflowSuiteScale (ratio)GeologyReservoir simulationFlow (mathematics)CarbonateRange (aeronautics)Reservoir modelingComputer sciencePetroleum engineeringEngineeringDatabaseCartography

Abstract

fetched live from OpenAlex

Summary We discuss the application of the new open-source Rapid Reservoir Modelling software (RRM) to create a suite of 3D reservoir models of a complex carbonate formation where each model is increasingly more refined such that progressively more small-scale geological structures are preserved. Using flow diagnostics we then calculate key metrics for the dynamic reservoir behaviour to quantify the similarities and dissimilarities of the flow behaviour across the different models. This analysis allows us to identify at which scale geological heterogeneities need to be resolved in the reservoir model to capture the essential flow behaviours. The workflow presented in this study hence allows us to efficiently and effectively test different geological concepts and analyse how multi-scale geological heterogeneities that may need to be represented in a reservoir model impact the predicted dynamic response, so as to design more reliable and robust reservoir models for a broad range of geoenergy applications.

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 score0.350
Threshold uncertainty score0.666

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 head0.068
GPT teacher head0.343
Teacher spread0.275 · 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

Citations2
Published2022
Admission routes2
Has abstractyes

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