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Record W1822934814 · doi:10.3968/5958

A Simulation Experimental Study on the Gas-Water Saturation Characteristics in the Process of Constant-Speed Gas Production Based on the Microscopic Model

2014· article· en· W1822934814 on OpenAlexvenueno aff
Xiaoyan Tang

Bibliographic record

VenueAdvances in petroleum exploration and development · 2014
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsSaturation (graph theory)Petroleum engineeringPermeability (electromagnetism)Relative permeabilityWet gasChemistryMechanicsGeotechnical engineeringGeologyPorosity

Abstract

fetched live from OpenAlex

This paper presents the important findings of research work that was undertaken on the residual water saturation of gas reservoirs that arises from the decrease in pore pressure during a constant-speed gas production process. To study the changes and distribution regularities of water-gas saturation, we used a micro-glass model to conduct a simulation study of the process of constant-speed gas production under pore pressures of 0.5 MPa, 1 MPa and 5 MPa. The results indicate that the total displacement power of the gas reservoir directly affects the development effect. The higher the pore pressures of the gas reservoir whose part of bound water may flow, the more difficult is the development of the reservoir due to the presence of more movable water, resulting in a poorer development effect. For a lower pore pressure of a gas reservoir, the bound water of the gas reservoir is generally immobile, that is, the gas production is single-phase gas seepage, resulting in an improved development effect. Implementing a reasonable development plan for a water-bearing gas reservoir with low permeability is clearly of great significance to ensure its effective development. Key words: Microscopic model; Constant speed; Gas production; Gas-water saturation; Variation characteristics; Simulation experiment

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 score0.201
Threshold uncertainty score0.290

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 head0.020
GPT teacher head0.275
Teacher spread0.256 · 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
Published2014
Admission routes1
Has abstractyes

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