Optimization of Enhanced Oil Recovery using Hybrid Molecular Dynamics and Reservoir Simulation for Nanoparticle-Stabilized Foam Flooding in High-Temperature High-Salinity Reservoirs

Project Abstract

In this study, we present a novel integrative approach that combines hybrid molecular dynamics (MD) simulations with advanced reservoir-scale modeling to optimize nanoparticle-stabilized foam (NSF) flooding for enhanced oil recovery (EOR) in high-temperature, high-salinity (HTHS) reservoirs. The work addresses the core challenge of foam stability and mobility control under harsh reservoir conditions by leveraging a multi-scale framework that links molecular interactions of nanoparticles, surfactants, and reservoir brine to macroscopic flow behavior and field-scale performance. We develop a coupled MD-continuum methodology where nanoparticle-surfactant complexes are characterized at the nanoscale to quantify interfacial tension, foam generation, and film durability in brine compositions representative of HTHS environments. These molecular insights feed into a dynamic foam rheology model and a two-phase flow simulator that captures foam generation, propagation, coalescence, and blockage effects under varying pore throat geometries, temperatures up to 180–250°C, and salinity up to several tens of grams per liter. The reservoir model integrates dual-porosity, dual-permeability concepts to reflect fractured and heterogeneous formations, enabling assessment of foam efficacy in mobility control and sweep efficiency across vertical and horizontal heterogeneities. We systematically study the effects of nanoparticle size, surface functionalization, and concentration on NSF stability, adsorption losses, and oil displacement efficiency. A parametric screening identifies optimal nanoparticle types (e.g., silica, silica-alumina, and ceramic variants), surfactant-nanoparticle synergy, and brine composition that maximize interfacial viscoelastic properties and resist soap washing while maintaining injectivity. The methodology includes experimental validation through high-temperature high-pressure core floods and interfacial tensiometry, informing the calibration of the MD-informed foam parameters and the permeability reduction phenomenon observed in simulations. We apply sensitivity analyses and uncertainty quantification to distinguish the relative importance of chemical, thermal, and hydrodynamic factors, providing probabilistic performance envelopes for forecasted field outcomes. Key outcomes include (i) a validated multi-scale model that can predict NSF stability, foam generation, and oil displacement under HTHS conditions; (ii) a design framework for selecting nanoparticle-surfactant systems tailored to reservoir temperature, salinity, and rock type; (iii) guidelines for controlling foam mobility to reduce gas breakthrough and achieve favorable conformance; and (iv) a roadmap for pilot-scale implementation that accounts for injectivity constraints, environmental considerations, and economic viability. The research contributes to the advancement of EOR strategies in challenging reservoirs by delivering a robust, transferable simulation framework that couples molecular-scale phenomena with field-scale performance predictions, enabling informed decision-making for optimized NSF flooding operations.

Project Overview

What This Project Is About

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A straightforward exploration of how combining computer simulations with practical flood tests can improve oil recovery. The project studies the use of nanoparticles to stabilize foam that blocks water pathways in oil reservoirs, and how mixing physics-based simulations with reservoir models helps predict performance in tough conditions like high temperature and high salinity.

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The Problem It Addresses

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Many oil reservoirs lose pressure and yield before all oil is recovered, especially where water floods are less effective due to messy pore spaces and harsh underground conditions. Foam-based methods show promise, but their behavior in challenging environments is hard to predict. This project tackles the gap between small-scale experiments, computer models, and real-field conditions to improve decision-making.

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Objectives of the Project

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1. Explain how nanoparticles help stabilize foam in challenging reservoirs.
2. Combine simple fluid models with detailed molecular simulations to capture foam behavior.
3. Assess the impact of high temperature and high salinity on foam stability and oil recovery.
4. Develop a user-friendly workflow to predict foam performance in reservoirs.

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What You Will Do Step by Step

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1. Review basic concepts of enhanced oil recovery and foam flooding.
2. Learn simple simulation tools and basic molecular dynamics principles.
3. Set up small-scale experiments or data collection for nanoparticle-stabilized foam.
4. Run integrated simulations combining reservoir geometry, foam dynamics, and nanoparticle effects.
5. Analyze results to identify key factors that boost oil recovery.
6. Validate models with available data and adjust assumptions as needed.
7. Document the workflow for reproducibility and future work.
8. Prepare a concise guidelines report for industry relevance.

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Expected Outcome

Clear insights into which nanoparticles and foam conditions work best in difficult reservoirs, plus a practical modeling workflow that engineers can use to forecast oil recovery improvements under high-temperature and high-salinity settings.