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Record W2300188000 · doi:10.2118/178911-pa

Flow in Linear Composite Reservoirs

2015· article· en· W2300188000 on OpenAlexafffund
Etim H. Idorenyin, Ezeddin Shirif

Bibliographic record

VenueSPE Reservoir Evaluation & Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Regina
FundersUniversity of AlbertaUniversity of Regina
KeywordsFlow (mathematics)MechanicsComposite numberSeries (stratigraphy)Constant (computer programming)Interface (matter)InverseComputer scienceGeologyMathematicsAlgorithmGeometryPhysics

Abstract

fetched live from OpenAlex

Summary This study presents a fast and accurate closed-form, fully analytical solution for modeling fluid flow in linear composite reservoirs. A linear composite system, as mentioned here, refers to a porous medium that can be represented as a linear assembly of distinct homogeneous regions. It is assumed that adjacent regions are connected along an interface of pressure and flux continuity. The solution presented here differs from known analytical models in literature because it does not contain an infinite series that often takes a toll on computational time, especially when accuracy is of prime importance. Thus, this solution finds great use in inverse problems encountered in both rate and pressure transient analyses primarily because of its accuracy and the relatively short computational time required. Depending on the values of the interface coefficients (key parameters in our solution), infinite boundaries, no-flow boundaries, constant-pressure boundaries, and transition interfaces (that is, partially sealing boundaries) can be represented in our model without much computational effort.

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)0.001
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: none
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.035
GPT teacher head0.281
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

Citations15
Published2015
Admission routes2
Has abstractyes

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