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Record W2287183046 · doi:10.2118/178964-ms

Experimental and Numerical Investigation of a Novel Technique for Perforation in Petroleum Reservoir

2016· article· en· W2287183046 on OpenAlexaff
Liancun Zheng, Mohammad Azizur Rahman, Mohammad Jalal Ahammad, Stephen Butt, Jahrul Alam

Bibliographic record

VenueSPE International Conference and Exhibition on Formation Damage Control · 2016
Typearticle
Languageen
FieldEngineering
TopicDam Engineering and Safety
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsPetroleumPetroleum engineeringResidual oilPerforationResidualComputer simulationStage (stratigraphy)Computer sciencePorous mediumProcess (computing)PorosityGeologyEngineeringGeotechnical engineeringSimulationMechanical engineeringAlgorithm

Abstract

fetched live from OpenAlex

Abstract Formation damage in petroleum reservoirs is a combination of many complicated phenomena. The formation damage reduces the productivity index significantly. The damage mechanisms may occur from the stage of drilling process to tertiary oil recovery stage. This study investigates the formation damage of petroleum wells due to shooting in well completion. The study is conducted with numerical and experimental investigations. The experimental set is a prototype. Two techniques are considered for numerical simulations: one is ANSYS-CFX and other one is a computational methodology which is based on weighted residual collocation method. Single phase and two-phase flows in porous media have been taken into consideration to explore the productivity index, which is affected by formation damage. Therefore, a better understanding of damage mechanisms for various reservoir conditions can lead to optimize oil recovery rate.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.779
Threshold uncertainty score0.310

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 head0.022
GPT teacher head0.249
Teacher spread0.227 · 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 designBench or experimental
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

Citations10
Published2016
Admission routes1
Has abstractyes

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