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Record W2896798842 · doi:10.2118/191540-ms

Computational Fluid Dynamics Modeling of Slip Flow Coupled with Gas Adsorption/Desorption Kinetics in Complex Pore Space

2018· article· en· W2896798842 on OpenAlexaff
Shanshan Yao, Ronny Pini, Xiangzeng Wang, Fanhua Zeng, Ning Ju

Bibliographic record

VenueSPE Annual Technical Conference and Exhibition · 2018
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsDesorptionAdsorptionSlip (aerodynamics)ThermodynamicsChemistryMethaneVolumetric flow rateMechanicsMaterials sciencePhysicsPhysical chemistry

Abstract

fetched live from OpenAlex

Abstract At reservoir conditions, gas flow confined in submicron pores of shale falls within slip flow and transition flow regimes. Beyond the common instant equilibrium assumption, we believe that gas adsorption/desorption on rough pore surfaces could be in non-equilibrium status when gas pressure keeps decreasing during production. We investigate the interplay of gas slip flow inside complex submicron-scale pores and gas adsorption/desorption kinetics on pore surfaces with computational fluid dynamics (CFD) under unsteady-state flow conditions. Different from previous studies, the gas adsorption/desorption is in non-equilibrium state, which is closer to real reservoir conditions. Given pore pressure Pp at time t, linear driving force model with gas desorption rate coefficient kd is applied to describe the difference between the equilibrium adsorption amount (calculated with adsorption isotherms) and the actual adsorption amount per unit pore surface area. Free gas flow inside 3D reconstructions of shale pore space is modeled by Navier-Stokes equations with Maxwell's first-order slip boundary conditions. To include gas contributions from desorption, extra source with strength equal to the gas desorption rate is added to the slip boundaries. Any type of adsorption isotherms can be incorporated into our CFD modeling. We investigate the coupling of slip flow and Langmuir adsorption isotherms for methane in 3D reconstructed pore space. We observe that not all of adsorbed gas measured in adsorption isotherms contribute to gas production. In our study the pore pressure, Pp, decreases along with time t. One significant finding is that there exists a key time point, tk, after which adsorbed gas starts desorbing off pore surfaces and the decreasing rate of pore pressure becomes smaller. The higher the gas desorption rate coefficient, kd, is, the earlier tk occurs. But the decreasing rate of pore pressure is no longer sensitive to the coefficient, kd, when kd is larger than 0.0005. Another significant finding is that optimum gas production rate should be chosen according to kd values. It is always better to have high production rate when kd is larger than 0.0005. If the kd is smaller than 0.0005, simulations are necessary to determine optimum gas production rate by balancing the development efficiency and final amount of gas desorption. Gas adsorption/desorption is always regarded as an instant equilibrium process in shale reservoir simulations. This study considers the non-equilibrium gas adsorption/desorption process, which is closer to real reservoir conditions. No studies in the literature have considered the influence of gas adsorption/desorption kinetics when choosing optimum production rates. CFD simulations in this study provide insight and guidelines on optimizing shale gas development with evaluating slip flow as well as gas adsorption/desorption characteristics.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.240
Teacher spread0.220 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2018
Admission routes1
Has abstractyes

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