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Record W4360848069 · doi:10.1021/acsomega.2c06943

Correlation Evaluation and Schematic Analysis of Influencing Factors Affecting Pore and Fracture Connectivity on the Microscale and Their Application Discussion in Coal Reservoir Based on X-ray CT Data

2023· article· en· W4360848069 on OpenAlexaff
Huihuang Fang, Zhangfei Wang, Shuxun Sang, Shiqi Liu, Chengchuan Gu, Jing Yang, Lei Li, Yanhui Huang

Bibliographic record

VenueACS Omega · 2023
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsUniversity of Saskatchewan
FundersNatural Science Foundation of Anhui ProvinceAnhui UniversityAnhui University of Science and TechnologyNational Natural Science Foundation of China
KeywordsSchematicMicroscale chemistryFracture (geology)CoalCorrelationGeologyEnvironmental sciencePetroleum engineeringMaterials scienceGeotechnical engineeringComputer sciencePsychologyEngineeringMathematicsGeometryWaste managementElectronic engineering

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide The connectivity of the pore/fracture system is the key to CO 2 injection and CH 4 production, which is of great significance in analyzing the correlation and weight of the influencing factors affecting the connectivity on the microscale. First, the 3D reconstruction of the coal reservoir is realized. Second, the characteristic parameters of pore/fracture structures are analyzed. Next, the characteristics of absolute permeability are analyzed, and then the correlation and weight analysis of the influencing factors are realized. Finally, the schematic analysis and application discussion of the influencing factors are carried out. The results show that porosity is the key factor restricting fluid migration. The heterogeneity of the reservoir can be characterized by the volume changes of the pore/fracture, organic matter, and mineral. The interconnected pores/fractures are mainly distributed in sheets and bands. The coordination number ranges from 1 to 15. The Ferret diameter is 0–10 μm. The tortuosity is 2.27111, 1.9034, 3.98522, and 3.51516, respectively, and the Euler characteristic number is 0.931868, 0.974719, 0.921144, and 0.897697, respectively. The permeability of the SH and YW samples is higher than that of the RL and PY samples. The single weight of the influencing factor is as follows: coordination number > Ferret diameter > Euler characteristic number > porosity > tortuosity. The analysis area of the comprehensive evaluation score of the influencing factors and the permeability value can be divided into three grades. There is a positive correlation among the coordination number, the quantity equilibrium of pores and throats, and the connectivity. The shape factor gradually increases with the increase of the Ferret diameter. The reservoir permeability is indirectly characterized by the coordination number, Ferret diameter, tortuosity, Euler characteristic number, and shape factor. This study can provide new ideas for clarifying the correlation degree and weight value of the characteristic parameters and can enrich the development of 3D digital core and CO 2 -ECBM 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)0.000
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: Empirical
Teacher disagreement score0.130
Threshold uncertainty score0.294

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.029
GPT teacher head0.280
Teacher spread0.251 · 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
Published2023
Admission routes1
Has abstractyes

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