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Record W2337258696 · doi:10.2118/180072-ms

A New Non-Darcy Flow Model for Low Velocity Multiphase Flow in Tight Reservoirs

2016· article· en· W2337258696 on OpenAlexfundno aff
Yi Xiong, Jinbiao Yu, Hongxia Sun, Jiangru Yuan, Zhaoqin Huang, Yu‐Shu Wu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsnot available
FundersCMG Reservoir Simulation FoundationColorado School of Mines
KeywordsPressure gradientMechanicsTight gasMultiphase flowDarcy's lawAdverse pressure gradientBoundary layerFluid dynamicsCapillary pressureFlow (mathematics)Porous mediumCapillary actionHele-Shaw flowFlow velocityExternal flowMaterials sciencePermeability (electromagnetism)Flow coefficientGeologyGeotechnical engineeringFlow separationOpen-channel flowPorosityPhysicsChemistryHydraulic fracturingComposite material

Abstract

fetched live from OpenAlex

Abstract This paper is to present a new non-linear flow model for low-velocity multiphase flow in tight petroleum reservoirs as well as its analytical and numerical solutions. The pore and pore-throat sizes of shale and tight-rock formations are on the order of tens of nanometers. The fluid flow in such small pores is significantly affected by walls of pores and pore-throats. This boundary-layer effect on fluid flow in tight rocks has been investigated through laboratory work. In analogue to flow through capillary tubes, it is found that the ratio of the thickness of boundary layer over the size of capillary tube is a function of pressure gradient; and the non-linear relationship between flow rate and pressure gradient is pronounced under the drive of small pressure gradient or low flow velocity. It is also observed that low permeability is associated with large boundary layer effect on fluid flow. Based on the studies of single-phase and multiphase flow though capillary tubes, the new non-Darcy flow model is proposed for describing multiphase flow in tight rock. The experimental results from a single capillary tube are extended to a bundle of tubes and finally to porous media of tight formations. A physics-based, non-Darcy low-velocity flow equation is derived to account for the boundary layer effect of tight reservoirs by adding a non-Darcy coefficient term, which is function of dimensionless thickness of boundary layer and pressure gradient. This non-Darcy equation describes the fluid flow more accurately for tight oil reservoir with low production rate and low pressure gradient as compared to laboratory observation. Both analytical and numerical solutions are obtained for the new non-Darcy flow model. First, a Buckley-Leverett type analytical solution is derived including gravity effect with this non-Darcy flow equation. Then, a numerical model has been developed for implementing this non-Darcy flow model for accurate simulation of multi-dimensional porous and fractured tight oil reservoirs. The sensitivity studies based on numerical simulations demonstrate the non-negligible effect of boundary layer on fluid flow in tight formations using an actual field example. Eventually, the experiment-based non-Darcy flow model could improve the forecast accuracy for long-term production rate and recovery factors of tight oil reservoirs. A new, physics-based low-velocity non-Darcy flow model is developed for description of single-phase and multiphase flow in tight reservoirs. In addition, both analytical and numerical solutions are provided for application of the new non-Darcy flow model for field studies. The results and knowledge obtained in this study may be applicable to both oil and gas flow in unconventional reservoirs.

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)0.001
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: none
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.232
Teacher spread0.221 · 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

Citations8
Published2016
Admission routes1
Has abstractyes

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