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Record W1968280852 · doi:10.2523/iptc-13164-ms

Three Dimensional Numerical Analysis of Fluid Flow through Fractured Rock Core Using X-Ray Computed Tomography

2009· article· en· W1968280852 on OpenAlexaff
Noriaki Watanabe, Yutaka Ohsaki, Tetsuya Tamagawa, Yoshihiro Tsuchiya, Nobuo Hirano, Hiroshi Okabe, Noriyoshi Tsuchiya

Bibliographic record

VenueInternational Petroleum Technology Conference · 2009
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsPetro-Canada
Fundersnot available
KeywordsFracture (geology)Aperture (computer memory)Materials scienceCalibrationFlow (mathematics)TomographyCalibration curveFluid dynamicsMechanicsGeologyComposite materialOpticsMathematicsPhysicsAcoustics

Abstract

fetched live from OpenAlex

Abstract A three dimensional numerical modeling coupled with X-ray Computed Tomography (CT) for fracture flow was applied to fractured granite core samples. One of the samples had an artificial single fracture, and the others had natural multiple fractures. A relationship between CT value and fracture aperture (fracture aperture calibration curve) was obtained by X-ray CT scanning for a fracture aperture calibration standard with varying the aperture from 0.1 to 0.5 mm. As a result, a linear relationship was obtained between CT value and fracture aperture. With the fracture aperture calibration curve, three dimensional distributions of CT values of the samples were converted into fracture aperture distributions to obtain fracture models of the samples. Porosities of the fracture models could provide good agreement with experimentally determined porosities for all the samples. By using the fracture models, a fluid flow simulation was also performed with a local cubic law-based fracture flow model. Numerical permeabilities by the flow simulation were much higher than experimentally determined permeabilities of the samples. It was however possible to match the numerical permeabilities with the experimental permeabilities for all the samples, by using a unique modification coefficient of 0.5 for the fracture aperture in the fracture flow model. With the modified flow model, it was possible to obtain detailed information of heterogeneities in fracture flow as well as permeabilities of the samples. Although the present X-ray CT scanning was performed at room temperature and pressure, it was expected that the numerical modeling had possibility to provide insights into heterogeneous nature of fracture flow in fractured reservoirs, such as channeling, as well as porosity and permeability, when the CT scanning was performed at reservoir conditions. Further studies on this kind of numerical modeling should provide simple and easy way to address heterogeneous fracture flow in reservoirs that can have impacts on productivity of wells, efficiency of recovery by water/gas flooding, and so on. Introduction Effective developments of oil/gas fractured reservoirs need well understanding of fracture flow characteristics in those reservoirs. Field and laboratory studies have suggested that fluid flow through a rock fracture is far from that of smooth parallel plates, due to channeling flow in a heterogeneous aperture distribution by rough surfaces [1–9]. When channeling flow occurs in a single fracture of granite, the area where flowing fluid exists is expected only 5–20% at confining pressures of up to 100 MPa, with various features in the preferential flow paths [9]. Against this background, we have recently developed a discrete fracture network model simulator, GeoFlow, in which fractures can have aperture distributions with natural heterogeneities [10]. Three dimensional fluid flow simulations for fracture networks by GeoFlow have demonstrated developments of three dimensional preferential flow paths in those fracture networks, and have suggested that performance of production well can be strongly affected by the heterogeneous fracture flow. Consequently, it should be considerably important to evaluate the heterogeneity and its impact for effective developments of fractured reservoirs.

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: none
Teacher disagreement score0.606
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.015
GPT teacher head0.249
Teacher spread0.235 · 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".

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Citations0
Published2009
Admission routes1
Has abstractyes

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