arXiv subjects

Matthias A. Cremon

Publications and source records attributed to Matthias A. Cremon.

4 recordsLinked to original sources

arXiv2024

Constrained Pressure-Temperature Residual (CPTR) Preconditioner Performance for Large-Scale Thermal CO2 Injection Simulation

This work studies the performance of a novel preconditioner, designed for thermal reservoir simulation cases and recently introduced in Roy et al. (2020) and Cremon et al. (2020), on large-scale thermal CO2 injection cases. For Carbon Capture and Sequestration (CCS) projects, injecting CO2 under supercritical conditions is typically tens of degrees colder than the reservoir temperature. Thermal effects can have a significant impact on the simulation results, but they also add many challenges for the solvers. More specifically, the usual combination of an iterative linear solver (such as GMRES) and the Constrained Pressure Residual (CPR) physics-based block-preconditioner is known to perform rather poorly or fail to converge when thermal effects play a significant role. The Constrained Pressure-Temperature Residual (CPTR) preconditioner retains the 2x2 block structure (elliptic/hyperbolic) of CPR but includes the temperature in the elliptic subsystem. The elliptic subsystem is now formed by two equations, and is dealt with by the system-solver of BoomerAMG (from the HYPRE library). Then a global smoother, ILU(0), is applied to the full system to handle the local, hyperbolic temperature fronts. We implemented CPTR in the multi-physics solver GEOS and present results on various large-scale thermal CCS simulation cases, including both Cartesian and fully unstructured meshes, up to tens of millions of degrees of freedom. The CPTR preconditioner severely reduces the number of GMRES iterations and the runtime, with cases timing out in 24h with CPR now requiring a few hours with CPTR. We present strong scaling results using hundreds of CPU cores for multiple cases, and show close to linear scaling. CPTR is also virtually insensitive to the thermal Peclet number (which compares advection and diffusion effects) and is suitable to any thermal regime.

physics.comp-ph↗

arXiv2021

Compositional Effects in Thermal, Compositional and Reactive Simulation

This work studies the influence of several compositional effects on thermal and reactive processes. First, the impact of using a fully compositional model in the context of thermal simulations is considered. Detailed phase behavior models rely on compositional descriptions of the oil using up to tens of components. Lumping a large number of components into a smaller number of pseudo-components in order to reduce the computational cost is standard practice for thermal simulations. Lumping schemes are typically calibrated using experimental data, in order to achieve a good approximation of the phase behavior of the initial oil. Due to the evaporation and condensation of components under thermal stimulation, the oil composition will widely vary in time and space. This works illustrates that even if the lumped schemes were able to capture the phase behavior of the initial oil, the lack of resolution can lead to modeling artefacts and/or fail to capture the relevant displacement processes. Then, the effects of different compositional interpretations of the lumped pseudo-species appearing in typical reaction schemes are investigated. A constant, mass-based fraction of the oil is allowed to react, and the reactive components are modified. Due to molecular weight effects, the reaction rate is larger when light and medium components are allowed to react. Those effects result in an increased displacement, leading to faster fronts, larger oil banks and more pressurization of the system.

physics.comp-ph↗

arXiv2020

Multi-level Delumping Strategy for Thermal Enhanced Oil Recovery Simulations at Low Pressure

We present a multi-level delumping method suitable for thermal enhanced oil recovery processes, for which hydrocarbon components are vaporized under high temperatures, move downstream in the gas phase and condense back to the liquid phase. To reduce the computational cost, it is standard practice to reduce the number of (pseudo-)components used in thermal reservoir simulation. Depending on the number and type of hydrocarbon pseudo-components retained in the simulations, we may not be able to capture the correct displacement due to large errors in the lumped phase behavior (flash) computations. We address that problem through a multi-level method: we use data obtained from a short simulation using the most detailed fluid description available, and leverage that information to guide a delumping process. We use temperature as a proxy variable for composition, and select reference temperatures. We extract the corresponding reference compositions from the detailed run and use them to extend the lumped pseudo-components to an approximate detailed composition. We test our method using six heavy oil samples, and under two different recovery processes: hot nitrogen injection and in-situ combustion (air injection and exothermic oxidation reactions). The average error on the liquid mole fraction is reduced by 4-12 times (depending on the oil samples) compared to the flash using pseudo-components, and the maximum error by 6-48 times. We illustrate that the method is amenable to manually adding more information about the physics of some oil samples. We also discuss how to efficiently pick the reference temperatures. For uniformly sampled temperatures (between a minimum and maximum temperature), we conduct a sensitivity study which led us to use six temperatures. We ran both local (Pattern Search, PS) and global (Particle Swarm Optimization, PSO) gradient-free optimization methods.

physics.comp-ph↗

arXiv2020

Multi-Stage Preconditioners for Thermal-Compositional-Reactive Flow in Porous Media

We present a family of multi-stage preconditioners for coupled thermal-compositional-reactive reservoir simulation problems. The most common preconditioner used in industrial practice, the Constrained Pressure Residual (CPR) method, was designed for isothermal models and does not offer a specific strategy for the energy equation. For thermal simulations, inadequate treatment of the temperature unknown can cause severe convergence degradation. When strong thermal diffusion is present, the energy equation exhibits significant elliptic behavior that cannot be accurately corrected by CPR's second stage. In this work, we use Schur-complement decompositions to extract a temperature subsystem and apply an Algebraic MultiGrid (AMG) approximation as an additional preconditioning stage to improve the treatment of the energy equation. We present results for several two-dimensional hot air injection problems using an extra heavy oil, including challenging reactive In-Situ Combustion (ISC) cases. We show improved performance and robustness across different thermal regimes, from advection dominated (high Peclet number) to diffusion dominated (low Peclet number). The number of linear iterations is reduced by 40-85% compared to standard CPR for both homogeneous and heterogeneous media, and the new methods exhibit almost no sensitivity to the thermal regime.

math.NA↗