A Streamline Based Lagrangain Method to Investigate Two-Phase Flow in Hydrocarbon Recovery

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The study of miscible flow is an important topic in the field of petroleum science and engineering. The miscible displacement of hydrocarbon-like oil by a solvent such as carbon dioxide helps to enhance the recovery of the hydrocarbon. The use of CO2 helps to reduce the viscosity of resident fluids like oil, In addition, the use of CO2 would reduce its accumulation in the atmosphere to help a green environment. The production of hydrocarbons in the secondary or tertiary phase follows the mostly miscible flow. The Computational Fluid Dynamics (CFD) simulations can help with better understanding enchanch oil recovery in the two-phase flow system. The traditional simulations of such flow suffer numerical artifacts due to the appropriate methods. We present a generalized CFD model for studying the transient methodology for momentum transfer. We investigate an upscaling approach to resolve the small-scale features of miscible flow in a porous medium. A streamline-based Lagrangian model is developed to study the displacement of oil by CO2 with sufficient accuracy and near-optimal computational cost.

Original languageEnglish
Title of host publicationInternational Petroleum Technology Conference, IPTC 2024
PublisherInternational Petroleum Technology Conference (IPTC)
ISBN (Electronic)9781959025184
DOIs
Publication statusPublished - 2024
Externally publishedYes
Event2024 International Petroleum Technology Conference, IPTC 2024 - Dhahran, Saudi Arabia
Duration: 12 Feb 2024 → …

Publication series

NameInternational Petroleum Technology Conference, IPTC 2024

Conference

Conference2024 International Petroleum Technology Conference, IPTC 2024
Country/TerritorySaudi Arabia
CityDhahran
Period12/02/24 → …

Keywords

  • Multiphase flow
  • Navier-Stokes Equation
  • Porous media
  • Streamline-based Lagrangian mothod
  • miscible flow

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