MétaCan
Menu
Back to cohort
Record W2947660725 · doi:10.3968/11002

Study on Development Law and Yield Replacement Method in Sazhong Development Area

2019· article· en· W2947660725 on OpenAlexvenueno aff
Shang Gao

Bibliographic record

VenueAdvances in petroleum exploration and development · 2019
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsWater cutYield (engineering)Production (economics)Environmental sciencePetroleum engineeringStage (stratigraphy)Oil productionWater developmentOil explorationEnvironmental engineeringHydrology (agriculture)EngineeringWater resourcesGeologyGeotechnical engineeringEconomicsEcology

Abstract

fetched live from OpenAlex

with the increasing time of water drive development in the Sazhong development zone, the oilfield has gradually entered the stage of high water cut stage or ultra-high water cut stage, and now it has entered the development stage of the high water cut period of the coexistence of the two drive. In order to extend the production life of the oilfield and plan the scale of production, based on the application of reservoir engineering method to calculate the natural decline rate and by grouping the Sa Pu oil layer and the Gaotaizi oil reservoir in the Sazhong oilfield, reducing the yield base of each year and re prediction of water drive production decline rule in Sazhong development area, which is concluded that the average decline rate is 7.46%. At the same time, the characteristics of tertiary oil recovery are analyzed, and the production replacement equation is established through the production demand under different production decline conditions. Based on the law of decreasing water drive and the change rule of chemical flooding production, the average chemical drive reserves in 12th Five-Year and the annual largest chemical drive reserves as tertiary oil recovery reserves, and the output forecast index of the sash development zone can be obtained.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.545
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.036
GPT teacher head0.303
Teacher spread0.267 · 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
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueAdvances in petroleum exploration and developmentSame topicReservoir Engineering and Simulation MethodsFrench-language works237,207