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Record W3148047800 · doi:10.2516/ogst/2021008

Editorial: Advanced modeling and simulation of flow in subsurface reservoirs with fractures and wells for a sustainable industry

2021· editorial· en· W3148047800 on OpenAlexaff
Shuyu Sun, Michael G. Edwards, Florian Frank, Jingfa Li, Amgad Salama, Bo Yu

Bibliographic record

VenueOil & Gas Science and Technology – Revue d’IFP Energies nouvelles · 2021
Typeeditorial
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Regina
FundersKing Abdullah University of Science and TechnologyNational Natural Science Foundation of China
KeywordsPetroleum engineeringGeologyFlow (mathematics)EngineeringEnvironmental scienceMining engineeringMechanicsPhysics

Abstract

fetched live from OpenAlex

Flow in subsurface reservoirs is a crucial process in a wide range of applications at different time and space scales, such as petroleum exploration and recovery, groundwater contamination, subsurface carbon sequestration, and geothermal reservoir engineering. As an effective method, modeling and simulation of flow in subsurface reservoirs become essential components of many scientific and
\nengineering applications in recent years. Significant advances have been made in this area, but accurate modeling and efficient, robust simulation still remain a challenging problem, especially for flow and transport in subsurface reservoirs with fractures and wells. To facilitate
\nthe exchange and dissemination of original research results and state-of-the-art reviews pertaining to flow in subsurface reservoirs with fractures and wells efficiently, we organized a special issue on “Advanced modeling and simulation of flow in subsurface reservoirs with fractures and wells for a sustainable industry” within Oil & Gas Science and Technology.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
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 score0.373
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0020.001
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 head0.004
GPT teacher head0.236
Teacher spread0.233 · 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.

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

Citations0
Published2021
Admission routes1
Has abstractyes

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