MétaCan
Menu
Back to cohort
Record W4404417732 · doi:10.3390/app142210534

Experimental Study on Interlayer Interference Characteristics During Commingled Production in a Multilayer Tight Sandstone Gas Reservoir

2024· article· en· W4404417732 on OpenAlexaff
Wen He, Jingjian Wang, Jiaojiao Liu, Daoyong Yang

Bibliographic record

VenueApplied Sciences · 2024
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsUniversity of Regina
FundersNational Natural Science Foundation of China
KeywordsPetroleum engineeringTight gasGeologyMaterials sciencePetrologyHydraulic fracturing

Abstract

fetched live from OpenAlex

In this study, a practical and comprehensive experimental technique has been proposed to investigate the interlayer interference characteristics in multilayer tight sandstone gas reservoirs with multi-pressure systems and different reserves. Firstly, single-layer depletion simulation experiments were conducted to measure the gas flow rate and gas extraction efficiency for each of the six layers. A series of physical simulation experiments were then conducted to monitor gas production and pressure variations in commingled multilayer production scenarios under various conditions. Finally, interlayer interference characteristics and gas extraction efficiencies and the main controlling factors were evaluated, analyzed, and identified. The interlayer pressure differential is found to be the primary factor dictating both interference and gas production, followed by initial gas production rates, and permeability variations in the order of positive significance. A higher interlayer pressure differential, a lower initial gas production rate, and a larger permeability variation result in an increase in interlayer interference and a reduction in gas production during commingled production. Increasing the number of commingled layers leads to an overall increase in gas production losses of 10.95% for two layers to 13.35% for four layers. Layers exhibiting small interlayer pressure difference are positively compatible for commingled production.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.283
Teacher spread0.253 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueApplied SciencesSame topicHydrocarbon exploration and reservoir analysisFrench-language works237,207