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Record W3198151891 · doi:10.3390/en14175488

Impact of Fracture Topology on the Fluid Flow Behavior of Naturally Fractured Reservoirs

2021· article· en· W3198151891 on OpenAlexfundno aff
L. L. Alvarez, Leonardo José do Nascimento Guimarães, Igor Fernandes Gomes, Leila Brunet de Sá Beserra, Leonardo Pereira, Tiago Siqueira de Miranda, Bruno Maciel, José Antônio Barbosa

Bibliographic record

VenueEnergies · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicGroundwater flow and contamination studies
Canadian institutionsnot available
FundersPetrobrasConselho Nacional de Desenvolvimento Científico e TecnológicoEnergi Simulation
KeywordsPermeability (electromagnetism)Fracture (geology)Fluid dynamicsPolygon meshGeologyMechanicsFinite element methodFlow (mathematics)Topology (electrical circuits)Matrix (chemical analysis)Network modelReservoir simulationRelative permeabilityPorous mediumGeotechnical engineeringPorosityGeometryComputer sciencePetroleum engineeringMathematicsStructural engineeringEngineeringMaterials sciencePhysicsChemistryArtificial intelligence

Abstract

fetched live from OpenAlex

Fluid flow modeling of naturally fractured reservoirs remains a challenge because of the complex nature of fracture systems controlled by various chemical and physical phenomena. A discrete fracture network (DFN) model represents an approach to capturing the relationship of fractures in a fracture system. Topology represents the connectivity aspect of the fracture planes, which have a fundamental role in flow simulation in geomaterials involving fractures and the rock matrix. Therefore, one of the most-used methods to treat fractured reservoirs is the double porosity-double permeability model. This approach requires the shape factor calculation, a key parameter used to determine the effects of coupled fracture-matrix fluid flow on the mass transfer between different domains. This paper presents a numerical investigation that aimed to evaluate the impact of fracture topology on the shape factor and equivalent permeability through hydraulic connectivity (f). This study was based on numerical simulations of flow performed in discrete fracture network (DFN) models embedded in finite element meshes (FEM). Modeled cases represent four hypothetical examples of fractured media and three real scenarios extracted from a Brazilian pre-salt carbonate reservoir model. We have compared the results of the numerical simulations with data obtained using Oda’s analytical model and Oda’s correction approach, considering the hydraulic connectivity f. The simulations showed that the equivalent permeability and the shape factor are strongly influenced by the hydraulic connectivity (f) in synthetic scenarios for X and Y-node topological patterns, which showed the higher value for f (0.81) and more expressive values for upscaled permeability (kx-node = 0.1151 and ky-node = 0.1153) and shape factor (25.6 and 14.5), respectively. We have shown that the analytical methods are not efficient for estimating the equivalent permeability of the fractured medium, including when these methods were corrected using topological aspects.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
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 head0.011
GPT teacher head0.257
Teacher spread0.246 · 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 source (direct Gemma or distilled Codex), 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

Citations18
Published2021
Admission routes1
Has abstractyes

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