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Record W1980863947 · doi:10.1080/10916466.2010.511384

The Relationship Between the Productivity Index and the Diffusivity Coefficient and Its Application in Reservoir Characterization

2012· article· en· W1980863947 on OpenAlex
Alireza Qazvini Firouz, Benyamin Yadali Jamaloei, Farshid Torabi, Vahid Dehdari

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePetroleum Science and Technology · 2012
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsPetrophysicsThermal diffusivityWellborePetroleum engineeringPermeability (electromagnetism)Reservoir modelingIndex (typography)SkewnessDrawdown (hydrology)GeologySoil scienceStatisticsMathematicsGeotechnical engineeringThermodynamicsComputer scienceChemistryPorosity

Abstract

fetched live from OpenAlex

Abstract The authors offer a reliable means to determine the diffusivity coefficient and permeability distribution throughout a reservoir while minimizing the number of expensive well tests. To explain this inexpensive procedure, data of the Asmari Reservoir—located in Iran on the Coast of Persian Gulf—are used. Using well test, petrophysical, pressure-volume-temperature, and production data, a relationship between the productivity index and the diffusivity coefficient is established for the Asmari Reservoir without relying on the reservoir radius value, which is uncertain when obtained from drawdown well testing. This method determines the optimum locations for new wells in development of giant fields.

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.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score0.511

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.010
GPT teacher head0.230
Teacher spread0.220 · 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