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Investigation of Methane Desorption and Its Effect on the Gas Production Process from Shale: Experimental and Mathematical Study

2016· article· en· W2558639589 on OpenAlexaff
Jinjie Wang, Mingzhe Dong, Zehao Yang, Houjian Gong, Yajun Li

Bibliographic record

VenueEnergy & Fuels · 2016
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsUniversity of Calgary
FundersMinistry of Science and Technology of the People's Republic of China
KeywordsDesorptionMethaneAdsorptionChemistryOil shaleDiffusionPetroleum engineeringNatural gasEnvironmental scienceThermodynamicsGeologyOrganic chemistry

Abstract

fetched live from OpenAlex

A number of potential gas reserves are present in shale reservoirs around the world. The gas production process is more complex in shale reservoirs than in conventional reservoirs, mainly because of the ultralow permeability of the matrix, complex pore structure, and organic components that cause desorption. Long-term gas production comes from both free gas expansion and adsorbed gas desorption. In addition to the estimation of the gas content reserve in a reservoir, an accurate description of the effect of adsorbed gas on the dynamic gas production process is meaningful for predicting gas production from shale. Experimental and mathematical efforts have been undertaken to obtain an accurate description of the gas production process. This paper investigates desorption and diffusion, which are two of the key mechanisms in the shale matrix during gas production. The experimental results suggest that the gas production process from shale can be divided into two stages: free gas expansion from inorganic micropores in the early stage and the subsequent desorption–diffusion-dominated stage in the matrix. Furthermore, the effects of production pressure (equal to the external pressure), temperature, and particle diameter on the dynamic gas production process were examined based on the variable-volume volumetric method (VVM). Both higher external pressure and higher temperature lead to the lower contribution of desorbed gas to the total gas production. Moreover, the delayed adsorption diffusion model, which considers the dynamic gas desorption/adsorption, is presented to adequately represent the measured dynamic experimental data. The calculated gas production curves are in good agreement with experimental observations. Mathematical calculations of the production rate also suggest and confirm the two-stage process of gas production. This paper enables operators to develop a primary understanding of how gas desorption affects the performance of a shale gas well and provides insights into the analysis of the gas flow regime and more accurate forecasting for shale gas production.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.201

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 head0.019
GPT teacher head0.241
Teacher spread0.222 · 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 designBench or experimental
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

Citations51
Published2016
Admission routes1
Has abstractyes

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