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Record W4233524370 · doi:10.1504/ijpe.2016.081789

Analytical coupled axial-radial semi-steady state productivity model for horizontal wells in anisotropic medium

2016· article· en· W4233524370 on OpenAlexaff
Thormod E. Johansen, Jie Cao, Lesley James

Bibliographic record

VenueInternational Journal of Petroleum Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMechanicsInflowFlow (mathematics)AnisotropyQuadratic equationAxial compressorGeologyMathematicsGeometryPhysicsThermodynamicsOptics

Abstract

fetched live from OpenAlex

The classical productivity models are solutions to one-dimensional radial flow equations for vertical wells at different flow conditions. A new, fully analytical model for coupled radial well inflow and axial reservoir flow has been developed under the assumption of semi-steady state flow, i.e. the axial flow and the well inflow are solved simultaneously in closed form expressions. This coupled axial-radial flow model results in a quadratic pressure profile in the axial direction and a quadratic-logarithmic pressure profile in the radial direction. The new productivity equations are formulated by using either external pressure or average reservoir pressure in addition to flowing wellbore pressure. For the special case where axial flow or radial flow is zero, it is shown that the formulas coincide with the classical formulas for radial and linear flow, respectively. The new productivity model is also developed for an anisotropic medium by implementing a space transformation in the near-well region. The anisotropic productivity model highlights the flow in the near-well region and provides flexibility in choosing configuration of near-well simulation grid blocks. An algorithm for the simulation of the near-well flow and horizontal well productivity 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 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)0.001
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 score0.527
Threshold uncertainty score0.750

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.015
GPT teacher head0.269
Teacher spread0.254 · 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

Citations4
Published2016
Admission routes1
Has abstractyes

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