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Record W2120815905 · doi:10.5539/eer.v2n1p107

Research on New Method of Clastic Reservoir Permeability Interpretation

2012· article· en· W2120815905 on OpenAlexvenueno aff
Wei Liu, Hong Fang, Xing Zhang

Bibliographic record

VenueEnergy and Environment Research · 2012
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsnot available
Fundersnot available
KeywordsTortuosityPermeability (electromagnetism)Hagen–Poiseuille equationPorosityApproximation errorInterpretation (philosophy)GeologyMaterials scienceSoil scienceMechanicsMathematicsFlow (mathematics)Geotechnical engineeringComputer scienceGeometryMathematical analysisChemistryPhysics

Abstract

fetched live from OpenAlex

Reservoir permeability is an important parameter in reservoir evaluation and the research on surplus oil distributing regularities. However, it is difficult to calculate it accurately in the process of reservoir interpretation. The ordinary interpretation model uses the rough linear relationship between porosity and permeability within a semi-log coordinate system, resulting in much error. In this paper the permeability calculating formula, including three parameters, porosity, pore-throat radius and pore tortuosity, was deduced from the unity of Poiseuille Capillary Model and Darcy’s Law. Based on that, data of core physical properties analysis, mercury injection and well logging were used to construct the empirical relationship between pore tortuosity and pore-throat radius, thus realizing the transformation of permeability calculation from the solo empirical model to the semi-theoretical and semi-empirical model. The calculated results showed that the relative error of the new model was 20.26%, with 22.46 percentage points lower than the error of the traditional empirical model.

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)0.003
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 score0.226
Threshold uncertainty score0.515

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 head0.064
GPT teacher head0.355
Teacher spread0.291 · 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

Citations1
Published2012
Admission routes1
Has abstractyes

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