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Record W2907702233 · doi:10.3968/7430

The Rapid Quantitative Description of the Remaining Oil

2015· article· en· W2907702233 on OpenAlexvenueno aff
Liu Jiyu, Mingxi Pan, Wei Dong, Haili You

Bibliographic record

VenueAdvances in petroleum exploration and development · 2015
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsPetroleum engineeringOil reservesGeologyOil explorationOil productionDistribution (mathematics)Quantitative assessmentStage (stratigraphy)Fossil fuelEnvironmental scienceEngineeringPetroleumMathematicsPaleontologyReliability engineeringWaste management

Abstract

fetched live from OpenAlex

At the late development stage of an oilfield, the distribution and potential evaluation of remaining oil is the key to comprehensive adjustment. Only if the location and quantity of the remaining oil is clear, can the targeted adjustment measures be made. Therefore, the quantitative description of remaining oil has become a significant issue to be solved in the middle-late period of oilfield development. In this paper, based on the concept of remaining oil, a new calculation principle of remaining geological reserves is determined - the difference between geological reserves and the cumulative oil production. The quantitative description of remaining geological reserves is refined into the single sand body. Moreover, based on the development condition of the study area, the remaining geological reserves distribution is described in phases. The new method is practically applied in a study area, of which the result is compared with the numerical simulation, and the precision is high. This method has good accuracy, can quickly quantitatively describe remaining oil distribution, and also has high practical value.

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 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: none
Teacher disagreement score0.804
Threshold uncertainty score0.186

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 communication0.0000.000
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 head0.063
GPT teacher head0.293
Teacher spread0.230 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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