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Record W4229333562 · doi:10.3390/geosciences12050201

Review of Petroleum and Hydrogeology Equations for Characterizing the Pressure Front Diffusion during Pumping Tests

2022· article· en· W4229333562 on OpenAlexaff
Daouda Méité, Silvain Rafini, Romain Chesnaux, Anouck Ferroud

Bibliographic record

VenueGeosciences · 2022
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsHydrogeologyAquiferAquifer testDrawdown (hydrology)Front (military)GeologyMechanicsGeotechnical engineeringWell test (oil and gas)Test dataDiffusion equationEngineeringGroundwater rechargePetroleum engineeringGroundwaterPhysics

Abstract

fetched live from OpenAlex

In hydrogeology, the pressure front diffusion equation is crucial for the interpretation of pumping tests. It describes the displacement around the pumping well of the pressure front generated by a hydraulic disturbance, such as pumping or injection. This equation serves to physically locate the hydraulic objects (the recharge boundary, impermeable boundary, fault and hydraulic connection) that are able to influence the hydrodynamic behaviour of aquifers during a transient hydraulic test. However, several authors who have attempted to characterize this equation have come up with different expressions because the pressure front has been defined according to different approaches. This paper first clarifies the origin of the divergence between authors before reviewing seven approaches used to characterize the diffusion equation. In addition, a new approach is proposed, which is more practical and which defines the pressure front using the logarithmic derivative of the drawdown. Finally, all these reviewed approaches, plus the new one, are unified into a single general approach that defines the pressure front according to a general criterion, which is the critical drawdown, noted as sc. To do this, the pressure front criteria of each existing approach, including the new one, were converted into equivalent critical drawdowns. The ultimate goal of this study is to enable hydrogeologists to use all these approaches correctly in order to improve the accuracy of the interpretation of pumping test data for the better characterization of the geometry of aquifers.

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)0.000
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 score0.169
Threshold uncertainty score0.356

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.010
GPT teacher head0.227
Teacher spread0.217 · 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

Citations6
Published2022
Admission routes1
Has abstractyes

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