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Record W4407130063 · doi:10.1016/j.energy.2025.134867

Maturity-dependent thermodynamic and flow characteristics in continental shale oils

2025· article· en· W4407130063 on OpenAlexaff
Yilei Song, Zhaojie Song, Yasi Mo, Yufan Meng, Shouceng Tian, Zhangxin Chen

Bibliographic record

VenueEnergy · 2025
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsUniversity of Calgary
FundersChina National Petroleum CorporationScience Foundation of China University of Petroleum, BeijingChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsMaturity (psychological)Oil shalePetroleum engineeringFlow (mathematics)Flow propertiesGeologyEnvironmental scienceThermodynamicsMechanicsPhysicsPaleontologyPolitical science

Abstract

fetched live from OpenAlex

Understanding the phase behavior and flow characteristics of shale oil is crucial for optimizing exploration and development strategies. This study examines the thermodynamic properties and flow capacities of shale oils from a continental freshwater reservoir in northeastern China (GNE), comparing them with oils from a continental saline shale oil reservoir in northwestern China (JNW) and the marine Bakken reservoir in North America. Experiments, including degassing, constant composition expansion, and viscosity measurements, combined with phase behavior modeling and pore network simulations, reveal that high-maturity shale oils contain more light fractions, exhibit higher light-to-heavy ratios, elevated bubble point pressures , and gas-oil ratios, along with lower densities and viscosities. These characteristics result in superior flow capacities, as indicated by a shift to the upper left in P-T phase diagrams . Key physical properties such as gas-to-oil ratio, density, and flow rate are strongly correlated with the light-to-heavy ratio, making it a critical parameter for shale oil classification. Notably, JNW shale oil , characterized by an extremely low light-to-heavy ratio, shows markedly different properties compared to GNE and Bakken shale oils. These findings highlight the need for tailored development strategies, such as early pressure maintenance in high-maturity reservoirs, to enhance recovery efficiency.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.832
Threshold uncertainty score0.299

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 head0.004
GPT teacher head0.193
Teacher spread0.190 · 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

Citations9
Published2025
Admission routes1
Has abstractyes

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