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DOE OSTI2013

Computational Biorheology of Human Blood Flow in Health and Disease

Hematologic disorders arising from infectious diseases, hereditary factors and environmental influences can lead to, and can be influenced by, significant changes in the shape, mechanical and physical properties of red blood cells (RBCs), and the biorheology of blood flow. Hence, modeling of hematologic disorders should take into account the multiphase nature of blood flow, especially in arterioles and capillaries. We present here an overview of a general computational framework based on dissipative particle dynamics (DPD) which has broad applicability in cell biophysics with implications for diagnostics, therapeutics and drug efficacy assessments for a wide variety of human diseases. This computational approach, validated by independent experimental results, is capable of modeling the biorheology of whole blood and its individual components during blood flow so as to investigate cell mechanistic processes in health and disease. DPD is a Lagrangian method that can be derived from systematic coarse-graining of molecular dynamics but can scale efficiently up to arterioles and can also be used to model RBCs down to the spectrin level. We start from experimental measurements of a single RBC to extract the relevant biophysical parameters, using single-cell measurements involving such methods as optical tweezers, atomic force microscopy and micropipette aspiration, and cell-population experiments involving microfluidic devices. We then use these validated RBC models to predict the biorheological behavior of whole blood in healthy or pathological states, and compare the simulations with experimental results involving apparent viscosity and other relevant parameters. Finally, while the approach discussed here is sufficiently general to address a broad spectrum of hematologic disorders including certain types of cancer, this paper specifically deals with results obtained using this computational framework for blood flow in malaria and sickle cell anemia.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2013

Liquid and liquid–gas flows at all speeds

All speed flows and in particular low Mach number flow algorithms are addressed for the numerical approximation of the Kapila et al. [1] multiphase flow model. This model is valid for fluid mixtures evolving in mechanical equilibrium but out of temperature equilibrium and is efficient for material interfaces computation separating miscible and non-miscible fluids. In this context, the interface is considered as a numerically diffused zone, captured as well as all present waves (shocks, expansion waves). The same flow model can be used to solve cavitating and boiling flows [2]. Many applications occurring with liquid–gas interfaces and cavitating flows involve a very wide range of Mach number, from 10{sup −3} to supersonic (and even hypersonic) conditions with respect to the mixture sound speed. It is thus important to address numerical methods free of restrictions regarding the Mach number. To do this, a preconditioned Riemann solver is built and embedded into the Godunov explicit scheme. It is shown that this method converges to exact solutions but needs too small time steps to be efficient. An implicit version is then derived, first in one dimension and second in the frame of 2D unstructured meshes. Two-phase flow preconditioning is then addressed in the frame of the Saurel et al. [3] algorithm. Modifications of the preconditioned Riemann solver are needed and detailed. Convergence of both single phase and two-phase numerical solutions are demonstrated with the help of single phase and two-phase steady nozzle flow solutions. Last, the method is illustrated by the computation of real cavitating flows in Venturi nozzles. Vapour pocket size and instability frequencies are reproduced by the model and method without using any adjustable parameter.

42 ENGINEERING↗

DOE OSTI2021

Impact of particle loading and phase coupling on gas–solid flow dynamics: A case study of a two-phase, gas–solid flow in an annular pipe

The present study is devoted to a two-phase, gas–solid flow in an annular pipe (hollow cylinder) at an elevated pressure of 15 bars and moderate Reynolds number of circa 6000. The influence of the particle loading, the interaction between the phases, and turbulence dispersion on the flow dynamics is systematically studied by means of computational fluid dynamics simulations, employing the Ansys FLUENT commercial package. The cases with a particle volumetric fraction of 1.2% are referred to as “high particle loading,” and those with 0.13% are denoted as “low particle loading.” The following cases are investigated: (1) pure gas flow; (2) low particle loading two-phase flow with one-way coupling and with turbulence dispersion; (3) low particle loading two-phase flow with two-way coupling but without turbulence dispersion; (4) low particle loading two-phase flow with two-way coupling and with turbulence dispersion; (5) high particle loading two-phase flow with one-way coupling and with turbulence dispersion; (6) high particle loading two-phase flow with two-way coupling but without turbulence dispersion; and (7) high particle loading two-phase flow with two-way coupling and with turbulence dispersion. Additionally, the boundary layer is found to grow without fluctuations of the turbulent kinetic energy (TKE) for cases 1, 2, and 5. For case 4, the TKE fluctuations have been identified, although they appear to be less substantial than those in cases 6 and 7. The authors attribute the semi-chaotic nature of the TKE fluctuations to the particle loading and two-way coupling. In addition, the onset and development of the flow instability have been observed at a random axial distance in cases 4, 6, and 7. Such instability is also attributed to the two-way coupling with turbulence dispersion in the flow. It is concluded that the particle loading, one-way, or two-way coupling between the phases, and the turbulence dispersion models significantly influence the development of the flow dynamics with the same inlet and boundary conditions. Consequently, it is not a trivial question, which result a user should trust. The present computational results inspire to perform verification as well as experimental validation of the simulations, so the simulation results can subsequently be used with confidence for design analysis.

42 ENGINEERING↗

DOE OSTI2024

Fourier-MIONet: Fourier-enhanced multiple-input neural operators for multiphase modeling of geological carbon sequestration

Geologic carbon sequestration (GCS) is a safety-critical technology that aims to reduce the amount of carbon dioxide in the atmosphere, which also places high demands on reliability. Multiphase flow in porous media is essential to understand CO 2 migration and pressure fields in the subsurface associated with GCS. However, numerical simulation for such problems in 4D is computationally challenging and expensive, due to the multiphysics and multiscale nature of the highly nonlinear governing partial differential equations (PDEs). It prevents us from considering multiple subsurface scenarios and conducting real-time optimization. Here, we develop a Fourier-enhanced multiple-input neural operator (Fourier-MIONet) to learn the solution operator of the problem of multiphase flow in porous media. Fourier-MIONet utilizes the recently developed framework of the multiple-input deep neural operators (MIONet) and incorporates the Fourier neural operator (FNO) in the network architecture. Once Fourier-MIONet is trained, it can predict the evolution of saturation and pressure of the multiphase flow under various reservoir conditions, such as permeability and porosity heterogeneity, anisotropy, injection configurations, and multiphase flow properties. Compared to the enhanced FNO (U-FNO), the proposed Fourier-MIONet has 90% fewer unknown parameters, and it can be trained in significantly less time (about 3.5 times faster) with much lower CPU memory (<15%) and GPU memory (<35%) requirements, to achieve similar prediction accuracy. In addition to the lower computational cost, Fourier-MIONet can be trained with only 6 snapshots of time to predict the PDE solutions for 30 years. Furthermore, we observed that Fourier-MIONet can maintain good accuracy when predicting out-of-distribution (OOD) data. The excellent generalizability of Fourier-MIONet is enabled by its adherence to the physical principle that the solution to a PDE is continuous over time. Furthermore, the developed Fourier-MIONet makes it possible to solve the long-time evolution of geological carbon sequestration in a large-scale three-dimensional space accurately and efficiently.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

Lattice Boltzmann methods for multiphase flow and phase-change heat transfer

We report that over the past few decades, tremendous progress has been made in the development of particle-based discrete simulation methods versus the conventional continuum-based methods. In particular, the lattice Boltzmann (LB) method has evolved from a theoretical novelty to a ubiquitous, versatile and powerful computational methodology for both fundamental research and engineering applications. It is a kinetic-based mesoscopic approach that bridges the microscales and macroscales, which offers distinctive advantages in simulation fidelity and computational efficiency. Applications of the LB method are now found in a wide range of disciplines including physics, chemistry, materials, biomedicine and various branches of engineering. The present work provides a comprehensive review of the LB method for thermofluids and energy applications, focusing on multiphase flows, thermal flows and thermal multiphase flows with phase change. The review first covers the theoretical framework of the LB method, revealing certain inconsistencies and defects as well as common features of multiphase and thermal LB models. Recent developments in improving the thermodynamic and hydrodynamic consistency, reducing spurious currents, enhancing the numerical stability, etc., are highlighted. These efforts have put the LB method on a firmer theoretical foundation with enhanced LB models that can achieve larger liquid-gas density ratio, higher Reynolds number and flexible surface tension. Examples of applications are provided in fuel cells and batteries, droplet collision, boiling heat transfer and evaporation, and energy storage. Lastly, further developments and future prospect of the LB method are outlined for thermofluids and energy applications.

42 ENGINEERING↗

DOE OSTI2018

Multiphase Large Eddy Simulation of a Pulsed Sieve-Plate Extraction Column - 18125

Presented here is research resulting from a framework of single-phase and multiphase modelling trials. These are intended to accurately simulate the internal flow phenomena arising in pulsed sieve-plate extraction columns representative of those found in industry. Three-dimensional unsteady flow calculations using large eddy simulation coupled with dynamic sub-grid scale modelling have been used to capture the transient effects of sinusoidal flow conditions using the open source CFD code OpenFOAM{sup R}. Multiphase interface interactions are modelled using the volume of fluid method with appropriate heavy and light phase fluidic properties capturing surface tension, density and viscous effects. The system studied consists of a 150 mm diameter column with two perforated plates with a fractional free area of 25 %, operational under 1 Hz pulsing frequency at a 50 mm amplitude and plate spacing of 300 mm. The materials simulated include water (single-phase) and 3 M nitric acid and dodecane / tributyl phosphate mixture at 30 vol% (multiphase). Results are compared with appropriate correlations from literature. The dispersive mixing efficiency of the system was also evaluated via a mixing index value which allows quantification of the ratio of rotational and irrotational flow components. This study has found that the predictive methods employed are effective at capturing the different scales of turbulence present within pulsed sieve-plate extraction columns, and its influence on their operation. As such, the work highlights the ability of large eddy simulation-based modelling to predict the complex flows in such column geometries. Furthermore, the techniques employed provide valuable insight into the hydrodynamic flow characteristics and mixing mechanisms arising within pulsed sieve-plate extraction columns. (authors)

42 ENGINEERING↗

DOE OSTI2022

In situ feature analysis for large-scale multiphase flow simulations

The study of multiphase flow is essential for designing chemical reactors such as fluidized bed reactors (FBR), as a detailed understanding of hydrodynamics is critical for optimizing reactor performance and stability. An FBR allows scientists to conduct different types of chemical reactions involving multiphase materials, especially interaction between gas and solids. During such complex chemical processes, the formation of void regions in the reactor, generally termed as bubbles, is an important phenomenon. The study of these bubbles has a deep implication in predicting the reactor’s overall efficiency. But physical experiments needed to understand bubble dynamics are costly and non-trivial due to the technical difficulties involved and harsh working conditions of the reactors. Therefore, to study such chemical processes and bubble dynamics, a state-of-the-art computational simulation MFIX-Exa is being developed. Despite the proven accuracy of MFIX-Exa in modeling bubbling phenomena, the large-scale output data prohibits the use of traditional post hoc analysis capabilities in both storage and I/O time. Herein, to address these issues and allow the application scientists to explore the bubble dynamics in an efficient and timely manner, we have developed an end-to-end analytics pipeline that enables in situ detection of bubbles, followed by a flexible post hoc visual exploration methodology of bubble dynamics. The proposed method enables interactive analysis of bubbles, along with quantification of several bubble characteristics, enabling experts to understand the bubble interactions in detail. Positive feedback from the experts has indicated the efficacy of the proposed approach for exploring bubble dynamics in very-large-scale multiphase flow simulations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

Coupling DAEM and CFD for simulating biomass fast pyrolysis in fluidized beds

We report results from computational simulations of an experimental, lab-scale bubbling bed biomass pyrolysis reactor that include a distributed activation energy model (DAEM) for the kinetics. In this study, we utilized multiphase computational fluid dynamics (CFD) to account for the turbulent hydrodynamics, and this was combined with the DAEM kinetics in a multi-component, multi-step reaction network. Our results indicate that it is possible to numerically integrate the coupled CFD–DAEM system without significantly increasing computational overhead. It is also clear, however, that reactor operating conditions, reaction kinetics, and multiphase flow dynamics all have major impacts on the pyrolysis products exiting the reactor. We find that, with the same pre-exponential factors and mean activation energies, inclusion of distributed activation energies in the kinetics can shift the predicted average value of the exit vapor-phase tar flux and its statistical distribution, compared to single-valued activation-energy kinetics. Perhaps the most interesting observed trend is that increasing the diversity of the DAEM activation energies appears to increase the mean tar yield, all else being equal. As a result, these findings imply that accurate resolution of the reaction activation energy distributions will be important for optimizing biomass pyrolysis processes.

09 BIOMASS FUELS↗

DOE OSTI2017

Enhancing the physical modeling capability of open-source MFIX-DEM software for handling particle size polydispersity: Implementation and validation

Multiphase flows are ubiquitous in many industrial processes. The inherent coupling of different phases poses many unique challenges in predicting and effectively controlling these processes. Hence, computational modeling and simulation offers a viable approach to overcome these challenges. Here in this study, we present recent development efforts for enhancing the physical modeling capabilities of an open-source computational modeling tool for real life industrial multiphase processes by enabling particle-size polydispersity and demonstrating with an associated validation study. The proposed implementation was performed in MFIX open-source framework due to its unique feature of tightly integrated computational fluid dynamics and discrete element method solvers for simulating coupled continuum fluid and granular flows. We have implemented the polydispersity feature in a minimally invasive way and provided means to allow easy specification of an arbitrary particle size distribution function, which also enables the user to easily handle an arbitrary number of solid phases, each possessing a distinct arbitrary particle-size distribution. To establish the credibility of improvements, we have carried out a preliminary verification and validation (V&V) study for the polydispersity feature by employing a hopper bin discharge problem, which is frequently encountered in industrial applications. Specifically, two types of micro-glass beads with distinct size distributions are used to fill the hopper in two possible packing arrangements, i.e., well-mixed and layered configurations, with varying mass (particle number) ratios. The experimentally obtained discharge dynamics (e.g., normalized discharge mass fraction for one of the phases versus the overall discharge mass fraction) for different systems is found to be in excellent agreement with the corresponding simulation results.

42 ENGINEERING↗

DOE OSTI2025

Modeling and simulation of multiphase flows

Multiphase flow can be found in a wide range of natural and industrial systems, such as sediment flow in rivers and coasts to pipeline transport, and reactors of all types. Computational fluid dynamics (CFD) modeling provides a rigorous physics-based method to visualize and quantify these flows and to assess their influence on their surroundings. This presentation will highlight computational research on two varied technologies in the energy sector: carbon capture and wellbore acoustics.

acoustic analysis↗

DOE OSTI2020

Multiphase Reacting Flow Simulations and Optimization of Commercial-Scale Aerobic Bioreactors

Aerobic bioreactors are widely used in the synthesis of value-added products in pharmaceutical and biotechnology industries, wherein microbial bioreactions aid in the conversion of sugars to macromolecules. These reactors are also being actively investigated as a cost-effective pathway for the production of low-value commodities such as biofuels and animal feed. Reactor scale-up is one of the important challenges when designing these systems. Gas and liquid phase transport, mass-transfer, and mixing physics at large length scales can significantly affect microbial conversion rates, particularly when the microbial reaction requires a narrow set of conditions. These phenomena are difficult to study in small-scale bench-top reactors that are typically well-mixed. Predictive computational fluid dynamics (CFD) based simulations can therefore aid in the design and optimization of these reactors. This work presents multiphase Euler-Euler CFD simulations of commercial-scale (~ 500 m3) aerobic bioreactors. Our mathematical model treats the gas and liquid as interpenetrating phases. This approach reduces the computational complexity of tracking individual gas bubbles that are several orders of magnitude smaller than reactor dimensions. We solve the Reynolds averaged Navier-Stokes (RANS) multiphase equations that account for phase and chemical species transport, interphase mass and momentum transfer and uses a phenomenological model for oxygen uptake by microbes. We use a customized solver derived from open-source CFD toolbox, OpenFOAM, to perform these simulations, which has been validated against small-scale reactors in our previous work. This work examines the performance of three different reactor designs, viz. bubble column reactor, airlift reactor with an internal draft tube, and a stirred-tank reactor with Rushton impellers. Reactor oxygen mass-transfer coefficient, gas hold-up, and oxygen distribution are critically analyzed among reactors, and sensitivity studies pertaining to gas flow rates and reactor geometry will be presented.

09 BIOMASS FUELS↗

DOE OSTI2016

Development of Multiple Solid Components Capability in GOTHIC Droplet Fields

GOTHIC is a versatile and generally applicable software package that solves complex thermal hydraulics problems. GOTHIC solves the conservation equations for mass, momentum and energy for multicomponent, multi-phase flow in lumped parameter and/or multi-dimensional geometries. GOTHIC version 8.2 includes a new feature to model one or more solid components in each of the modeled droplet fields. A solid component is a material present in the water which has volume, density, and heat capacity. GOTHIC has supported modeling multiple droplet fields since version 8.0 - each field has its own mass, energy, and momentum equation. Recent work by Lane et. al. allowed multiple solid components to be tracked in the continuous liquid field. With the release of version 8.2 the droplet fields can now include one or more solid components within each droplet field. The addition of solid components to the droplet field allows faithful simulation of one or more solid components throughout the liquid and droplet fields. Preliminary expected applications are for scenarios such as GSI-191 and longer term desired applications are aerosol simulation. (authors)

42 ENGINEERING↗

DOE OSTI2019

A GPU-accelerated package for simulation of flow in nanoporous source rocks with many-body dissipative particle dynamics

Mesoscopic simulations of hydrocarbon flow in source shales are concerning, in part due to the heterogeneous shale pores with sizes ranging from a few nanometers to a few micrometers. Additionally, the sub-continuum fluid–fluid and fluid–solid interactions in nano- to micro-scale shale pores, which are physically and chemically sophisticated, must be captured. To address those challenges, we present a GPU-accelerated package for simulation of flow in nano- to micro-pore networks with a many-body dissipative particle dynamics (mDPD) mesoscale model. Based on a fully distributed parallel paradigm, the code offloads all intensive workloads on GPUs. Other advancements, such as smart particle packing and no-slip boundary condition in complex pore geometries, are also implemented for the construction and the simulation of the realistic shale pores from 3D nanometer-resolution stack images. Our code is validated for accuracy and compared against the CPU counterpart for speedup. In our benchmark tests, the code delivers nearly perfect strong scaling and weak scaling (with up to 512 million particles) on up to 512 K20X GPUs on Oak Ridge National Laboratory’s (ORNL) Titan supercomputer. Moreover, a single-GPU benchmark on ORNL’s SummitDev and IBM’s AC922 suggests that the host-to-device NVLink can boost performance over PCIe by a remarkable 40%. Lastly, we demonstrate, through a flow simulation in realistic shale pores, that the CPU counterpart requires 840 Power9 cores to rival the performance delivered by our package with four V100 GPUs on ORNL’s Summit architecture. This simulation package enables quick-turnaround and high-throughput mesoscopic numerical simulations for exploring complex flow phenomena in nano- to micro-porous rocks with realistic pore geometries.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2017

Simulating Bubble Deformations Using the Intersection Marker (ISM) Front-Tracking method

Surface and volume tracking methods have been under continuous development over the last 50+ years since the first MAC (Marker and Cell) and SLIC (Simple Line Interface Calculation) methods appeared in the 1960's. The sustained interest in simulating multiphase flow stem from their many practical applications in the physical world; from biological systems, to chemical systems, to boiling dynamics and atmospheric heat and mass transfer. The ability to capture the details of interface evolution as well as the physics of interfacial heat and mass transfer has thus been a tempting goal for many numerical researchers. Numerical analysts, apart from the well-established and popular VOF PLIC (Volume of Fluid, Piecewise Linear Interface Construction) method, can now call upon even more computationally intensive options such as the LS (Level Set) method, the FT (Front Tracking) method, the MoF (Moment of Fluid) method and hybrid methods.

42 ENGINEERING↗

DOE OSTI2023

Implementation of a High-Fidelity Interface Resolving Method in Nek5000

The development and utilization of computational fluid dynamics (CFD) models for large, high-temperature electric melters in the Waste Treatment and Immobilization Plant (WTP) in eastern Washington State have proven to be valuable for various purposes. These models allow for a better understanding of the physio-chemical processes occurring within the melter vessels and can contribute to improving operational efficiency, throughput, and addressing operational issues related to vitrification. The CFD models employed for these melter vessels incorporate multiphase fluid flow and heat transfer simulations in different regions, including the plenum, cold cap, and molten glass regions. As the tank waste and glass formers are introduced into the melter, a reacting batch layer known as the cold cap forms on top of the molten glass. To enhance the melt rate, forced convection bubblers located at the bottom of the melters generate convection currents that help homogenize the molten glass and provide heat to the cold cap. As the bubbles rise through the highly viscous glass, they adopt a spherical-cap shape [1]. Meanwhile, the conversion of the batch to glass generates significant amounts of gases (such as water vapor, carbon dioxide, sulfur dioxide, and NOx) due to thermal decomposition [2]. These gases become trapped between the cold cap and molten glass, forming a foam layer [3]. For modeling multiphase flow in CFD and heat transfer simulations of waste glass melters at different scales, efforts are underway to augment the capabilities of the Nek5000 [4] and NekRS [5] open-source codes [6]. Nek5000/NekRS is a scalable and efficient spectral element code that has been successfully applied to a wide range of fluid dynamics problems. By leveraging the Nek5000/NekRS software, it becomes possible to model the melter systems more affordably and with lower computational requirements compared to currently utilized commercial CFD software. The specific objectives of this ongoing effort include: 1. Implementation of a level set method in Nek5000/NekRS: The level set method is a numerical technique commonly employed in CFD simulations to track and represent the interface between different phases or materials accurately. By incorporating this method into Nek5000/NekRS, the ability to simulate multiphase flows in waste glass melters at a high level of fidelity can be achieved. 2. Demonstration of capability for air bubbling through molten glass: As part of the development process, a specific case of air bubbling through molten glass will be simulated using the augmented Nek5000/NekRS code. This demonstration aims to showcase the ability of the software to accurately capture and analyze the complex phenomena involved in the multiphase flow within waste glass melters. By achieving these objectives, the improved Nek5000/NekRS code will offer a powerful computational tool for simulating and analyzing waste glass melter systems, enabling better understanding, optimization, and troubleshooting of these vitrification processes. The ability to accurately model and simulate multiphase flows has broad relevance across many industries and scientific domains, and the improved functionality can contribute to advancements in various fields beyond waste glass melter simulations.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2022

A particle resolved simulation approach for studying shock interactions with moving, colliding solid particles

This work applies a new combination of techniques for the fully resolved simulation of compressible, gas–particle multiphase flows. Here, the adaptive wavelet collocation method is used to dynamically, and efficiently, adapt the computational grid to localized flow features and the particles. A characteristic-based volume penalization method that imposes arbitrary Dirichlet, Neumann, or Robin-type immersed boundary conditions, is used to enforce the no-slip condition at particle surfaces. A hard-sphere collision model is applied to capture the particle–particle collisions. Proof of concept test cases are presented, showcasing the dynamic grid adaptation and fully resolved two-way coupling between the phases that is possible with this approach. Results for a shock-driven single cylinder under viscous and inviscid conditions are presented along with a demonstration of a shock interacting with a cloud of randomly distributed cylinders and spheres.

42 ENGINEERING↗

DOE OSTI2017

Development Optimization and Uncertainty Analysis Methods for Oil and Gas Reservoirs

Uncertainty complicates the development optimization of oil and gas exploration and production projects, but methods have been devised to analyze uncertainty and its impact on optimal decision-making. This paper compares two methods for development optimization and uncertainty analysis: Monte Carlo (MC) simulation and stochastic programming. Two example problems for a gas field development and an oilfield development are solved and discussed to elaborate the advantages and disadvantages of each method. Development optimization involves decisions regarding the configuration of initial capital investment and subsequent operational decisions. Uncertainty analysis involves the quantification of the impact of uncertain parameters on the optimum design concept. The gas field development problem is designed to highlight the differences in the implementation of the two methods and to show that both methods yield the exact same optimum design. The results show that both MC optimization and stochastic programming provide unique benefits, and that the choice of method depends on the goal of the analysis. While the MC method generates more useful information, along with the optimum design configuration, the stochastic programming method is more computationally efficient in determining the optimal solution. Reservoirs comprise multiple compartments and layers with multiphase flow of oil, water, and gas. We present a workflow for development optimization under uncertainty for these reservoirs, and solve an example on the design optimization of a multicompartment, multilayer oilfield development.

02 PETROLEUM↗

DOE OSTI2020

Coupled Time-Lapse Full-Waveform Inversion for Subsurface Flow Problems Using Intrusive Automatic Differentiation

We describe a novel framework for estimating subsurface properties, such as rock permeability and porosity, from time-lapse observed seismic data by coupling full-waveform inversion (FWI), subsurface flow processes, and rock physics models. For the inverse modeling, we handle the back propagation of gradients by an intrusive automatic differentiation strategy that offers three levels of user control: (1) At the wave physics level, we adopted the discrete adjoint method in order to use our existing high-performance FWI code; (2) at the rock physics level, we used built-in automatic differentiation operators from the TensorFlow backend; (3) at the flow physics level, we implemented customized partial differential equation (PDE) operators for the multiphase flow equations. The three-level coupled inversion strategy strikes a good balance between computational efficiency and programming efforts, and when the gradients are chained together, it constitutes a coupled inverse system. Our numerical experiments demonstrate that the three-level coupled inverse problem is superior in terms of accuracy to a traditional decoupled inversion strategy. Additionally, our method is able to simultaneously invert for parameters in empirical relationships such as the rock physics models. Our proposed inverted model can be used for reservoir performance prediction and reservoir management/optimization purposes.

54 ENVIRONMENTAL SCIENCES↗