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Record W2279152525 · doi:10.1504/ijpe.2015.073538

Analytical coupled axial and radial productivity model for steady-state flow in horizontal wells

2015· article· en· W2279152525 on OpenAlexafffund
Thormod E. Johansen, Lesley James, Jie Cao

Bibliographic record

VenueInternational Journal of Petroleum Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsInflowMechanicsPressure gradientLogarithmFlow (mathematics)Permeability (electromagnetism)MathematicsPhysicsMathematical analysisChemistry

Abstract

fetched live from OpenAlex

A new analytical model for coupled radial well inflow and axial flow has been developed and applied to horizontal well productivity calculations. This analytical model results in a linear pressure distribution in the axial direction and a logarithmic pressure distribution in the radial direction. The analytical solution is investigated for two special cases where a one-dimensional analytical solution already exists. It is proven that the model reduces to the classical radial inflow model in the case when the axial pressure gradient is zero and to the linear Darcy equation when there is zero radial inflow. The new equations are also used to evaluate the well flow rates when the skin effect is included. The analytical solution verifies that the axial flow in the reservoir, in general, cannot be ignored, particularly in reservoirs with high permeability and high well productivity. The analytical model is used to quantify the importance of frictional losses in well productivity calculations. An example on how the analytical model can be used in hand calculations to determine well productivity and frictional losses is presented.

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)0.001
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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.012
GPT teacher head0.234
Teacher spread0.222 · 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

Citations11
Published2015
Admission routes2
Has abstractyes

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