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Poroelastic Model for Production- and Injection-Induced Stresses in Reservoirs with Elastic Properties Different from the Surrounding Rock

2007· article· en· W2099400992 on OpenAlexafffund
Hamidreza Soltanzadeh, Chris Hawkes, Jitendra Sharma

Bibliographic record

VenueInternational Journal of Geomechanics · 2007
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPoromechanicsDimensionless quantityGeologyPoisson's ratioShear modulusGeotechnical engineeringShear (geology)Poisson distributionGeomechanicsModulusPlane stressSensitivity (control systems)MechanicsMaterials scienceGeometryPetrologyMathematicsStructural engineeringPhysicsEngineeringFinite element methodComposite materialPorosity

Abstract

fetched live from OpenAlex

Closed-form and semianalytical solutions for induced poroelastic stresses and strains are extremely useful for the design of subsurface fluid storage in caverns because of their relative ease of implementation and their suitability for parameter sensitivity analyses. This paper describes the use of Eshelby’s inhomogeneity theory to derive equations that can be used to predict the induced stresses and strains for reservoirs that are elliptical in cross section, under plane strain conditions. Sensitivity analyses demonstrate that the induced stresses are relatively insensitive to the Poisson’s ratio of the surrounding rock, but they are strongly affected by the Poisson’s ratio of the reservoir, and the ratio of shear modulus of the reservoir to that of the surrounding rock. Results are presented in terms of dimensionless parameters, which facilitate their application to a broad range of reservoir dimensions and pressure-change magnitudes. These equations can also be used to predict the induced stresses around a cavity, which represents the special case of an inhomogeneity with a shear modulus of zero.

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 score0.436
Threshold uncertainty score0.321

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 head0.029
GPT teacher head0.238
Teacher spread0.209 · 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

Citations27
Published2007
Admission routes2
Has abstractyes

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