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Record W2324696823 · doi:10.2118/175074-ms

Effect of Large Capillary Pressure on Fluid Flow and Transport in Stress-sensitive Tight Oil Reservoirs

2015· article· en· W2324696823 on OpenAlexfundno aff
Yi Xiong, Phil Winterfeld, Cong Wang, Zhaoqin Huang, Yu‐Shu Wu

Bibliographic record

VenueSPE Annual Technical Conference and Exhibition · 2015
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsnot available
FundersCMG Reservoir Simulation FoundationColorado School of Mines
KeywordsCapillary pressureOil shaleCapillary actionPore water pressureGeologyMultiphase flowEffective stressGeotechnical engineeringMechanicsGeomechanicsFluid dynamicsStress (linguistics)Permeability (electromagnetism)Deformation (meteorology)Materials scienceChemistryPorous mediumPorosityComposite material

Abstract

fetched live from OpenAlex

Abstract The pore sizes of unconventional reservoir rock, such as shale and tight rock, are on the order of nanometers. The thermodynamic properties of in-situ hydrocarbon mixtures in such small pores are significantly different from those of fluids in bulk size, primarily due to effect of large capillary pressure. For example, it has been recognized that the phase envelop shifts and bubble-point pressure is suppressed in tight and shale oil reservoirs. On the other hand, the stress-dependency is pronounced in low permeability rocks. It has been observed that pore sizes, especially the sizes of pore-throats, are subject to decrease due to rock deformation induced by the fluid depletion from over-pressurized tight and shale reservoirs. This reduction on pore spaces again affects the capillary pressure and therefore thermodynamic properties of reservoir fluids. Thus it is necessary to model the effect of stress- dependent capillary pressure and rock deformation on tight and shale reservoirs. In this paper, we propose and develop a multiphase, multidimensional compositional reservoir model to capture the effect of large capillary pressure on flow and transport in stress-sensitive unconventional reservoirs. The vapor-liquid equilibrium (VLE) calculation is performed with Peng-Robinson Equation of State (EOS), including the impact of capillary pressure on phase behavior and thermodynamic properties. The fluid flow is fully coupled with geomechanical model, which is derived from the thermo- poro-elasticity theory; mean normal stress as the stress variable is solved simultaneously with mass conservation equations. The finite-volume based numerical method, integrated finite difference method, is used for space discretization for both mass conservation and stress equations. The formulations are solved fully implicitly to assure the stability. We use Eagle Ford tight oil formations as an example to demonstrate the effect of capillary pressure on VLE. It shows that the bubble-point pressure is suppressed within nano-pores, and fluid properties, such as oil density and viscosity, are influenced by the suppression due to more light components remained in liquid phase. In order to illustrate the effect of stress-dependent capillary pressure on tight oil flow and production, we perform numerical studies on Bakken tight oil reservoirs. The simulation results show that bubble-point suppression is exaggerated by effects of rock deformation, and capillary pressure on VLE also affects the reservoir pressure and effective stress. Therefore the interactive effects between capillary pressure and rock deformation are observed in numerical results. Finally, the production performance in the simulation examples demonstrates the large effect of large capillary pressure on estimated ultimate recovery (EUR) in stress-sensitive tight 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 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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.146
Threshold uncertainty score0.504

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 head0.012
GPT teacher head0.247
Teacher spread0.235 · 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

Citations42
Published2015
Admission routes1
Has abstractyes

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