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All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Preface to the Special Issue on TOUGH Symposium 2015

The TOUGH Symposium 2015 was held in Berkeley, California, September 28-30, 2015. The TOUGH family of codes, developed at the Energy Geosciences Division of Lawrence Berkeley National Laboratory (LBNL), is a suite of computer programs for the simulation of multiphase and multicomponent fluid and heat flows in porous and fractured media with applications in many geosciences fields, such as geothermal reservoir engineering, nuclear waste disposal, geological carbon sequestration, oil and gas reservoirs, gas hydrate research, vadose zone hydrology and environmental remediation. Since the first release in the 1980s, many modifications and enhancements have been continuously made to TOUGH and its various descendants (iTOUGH2, TOUGH+, TOUGH-MP, TOUGHREACT, TOUGH+HYDRATE, TMVOC...), at LBNL and elsewhere. Today, these codes are used worldwide in academia, government organizations and private companies in problems involving coupled hydrological, thermal, biogeochemical and geomechanical processes. The Symposia, organized every 2-3 years, bring together developers and users for an open exchange on recent code enhancements and applications. In 2015, the Symposium was attended by one hundred participants, representing thirty-four nationalities. This Special Issue in Computers & Geosciences gathers extended versions of selected Symposium proceedings related to (i) recent enhancements to the TOUGH family of codes and (ii) coupled flow and geomechanics processes modeling. In this special issue, a paper by Jung et al. provides a new base version of TOUGH, TOUGH3. This latest version addresses the computational robustness and efficiency needed for the increasing complexity of the simulated processes and the growing size of model domains. Moreover, it consolidates the serial and parallel versions into a single code, hence, inconsistencies between different versions are less likely to occur, and code maintenance and development become more efficient. Finsterle et al. present a review of the capabilities of iTOUGH2 and illustrate the workflow of an integrated simulation-optimization framework. Liu et al. present an approach to reduce the computational cost of performing Bayesian inverse modeling and demonstrate their approach using a synthetic experiment simulated with TOUGH2. Regarding the mesh generation process, the paper by Doughty presents a generalization of the grid-generation algorithm embedded in TOUGH2 (MESHMAKER) to create one-column grids with fractal dimension, useful for representing flow through fractures or highly heterogeneous media with fractal geometry, and also for inverse methods. Sentís and Gable present a module to interface the open source code Lagrit with TOUGH2, that assists in the processes of mesh generation and model setup, and that can be particularly useful for complex geometries and large models. Bonduà et al. present a pre-processing tool (VORO2MESH) that features a full 3D Voronoi approach for structured and unstructured grids, and an enhanced post-processor (TOUGH2Viewer) for the visualization of 3D Voronoi grids. The literature review shows that coupled fluid flow and geomechanics has gained increasing attention in the last years in many geosciences fields. In this context, several codes of the TOUGH family have been successfully coupled with geomechanics software. The paper by Rutqvist presents a review of some of these TOUGH-based geomechanics models, of which the TOUGH-FLAC simulator – presented for the first time in the TOUGH Symposium 2003 – is well-known worldwide and has been used in a large variety of applications, encompassing the application domains of the TOUGH codes. Blanco-Martín et al. present an extension of TOUGH-FLAC to the finite strain framework, motivated by the existence of large strains associated with rock salt deformation in the context of heat-generating nuclear waste disposal. Following a paper published in the Special Issue of the TOUGH Symposium 2012 in this journal, Kim et al. present the current status of the TOUGH-RSBN simulator for coupled hydro-mechanical modeling of hydraulic fracturing using a discrete fracture network approach, and present some validation and demonstration cases. Rinaldi and Nespoli present TOUGH-SEED, an injection-induced seismicity modeling approach using TOUGH2 and a geomechanics stochastic model, and apply it to the Basel EGS project. Finally, Rinaldi et al. use the PEST protocol implemented in iTOUGH2, combined with TOUGH-FLAC as the forward model, to perform inverse modeling for the In Salah CO2 demonstration site in Algeria. Using pore pressures and InSAR displacements as observations, this approach allows for a multi-physics joint inversion and takes advantage of the inverse capabilities of iTOUGH2 (parameter estimation, sensitivity analysis, uncertainty estimation …), applied to coupled fluid flow and geomechanics. On behalf of the members of the TOUGH Symposium 2015 Organizing Committee, I hope that this collection of papers will provide the readers of Computers & Geosciences with a recent view of the capabilities of the TOUGH family of codes, as well as with current modeling research domains in the geosciences. Last but not least, I would like to express my gratitude to all the reviewers who kindly contributed to this Special Issue, as well as the Journal staff who have made this publication possible.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Optimization using pathwise algorithmic derivatives of electromagnetic shower simulations

Among the well-known methods to approximate derivatives of expectancies computed by Monte-Carlo simulations, averages of pathwise derivatives are often the easiest one to apply. Computing them via algorithmic differentiation typically does not require major manual analysis and rewriting of the code, even for very complex programs like simulations of particle-detector interactions in high-energy physics. However, the pathwise derivative estimator can be biased if there are discontinuities in the program, which may diminish its value for applications. This work integrates algorithmic differentiation into the electromagnetic shower simulation code HepEmShow based on G4HepEm, allowing us to study how well pathwise derivatives approximate derivatives of energy depositions in a sampling calorimeter with respect to parameters of the beam and geometry. We found that when multiple scattering is disabled in the simulation, means of pathwise derivatives converge quickly to their expected values, and these are close to the actual derivatives of the energy deposition. Additionally, we demonstrate the applicability of this novel gradient estimator for stochastic gradient-based optimization in a model example.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Producing High-fidelity Synthetic Population Ensembles at Scale

Used within social simulations, synthetic population ensembles enable uncertainty quantification (UQ) methods for obtaining more robust model inference and prediction. A synthetic population ensemble is a series of plausible virtual reconstructions of an area’s population at the granularity of people and residences, generated stochastically to preserve privacy of the source population survey’s respondents. In this paper, we demonstrate the production of large synthetic population ensembles for the US via Oak Ridge National Laboratory’s UrbanPop framework to support modeling of high spatial resolution energy affordability metrics from nationwide social surveys in collaboration with the fusionACS project. Our initial task involves creating ensembles for 17 US metropolitan areas, each consisting of 41 population instances (a base realization and 40 replicates). To accomplish this task at scale, we configured an integrated system comprised of a research cloud, virtual containerization, GPU-enhanced functionality, and a dual API/CLI to interact with UrbanPop’s maturing Likeness Python ecosystem. We observe a reduction in theoretical execution time while maintaining high-fidelity approximations of residential totals by metropolitan area and the demographic characteristics of neighborhoods. We discuss expansion of our approach to produce synthetic population ensembles for the entire US, particularly plans to establish automated workflows for job orchestration to increase computational efficiency, as well as provide outlook for broadening applications of the ensembles.

Gaboardi, James [ORNL] (ORCID:0000000247766826)↗
NASA NTRS1982

On-line methods for rotorcraft aeroelastic mode identification

The requirements for the on-line identification of rotorcraft aeroelastic blade modes from random response test data are presented. A recursive maximum likelihood (RML) technique is used in conjunction with a bandpass filter to identify isolated blade mode damping and frequency. The RML technique is demonstrated to have excellent convergence characteristics in random measurement noise and random process noise excitation. The RML identification technique uses an ARMA representation for the aeroelastic stochastic system and requires virtually no user interaction while providing accurate confidence bands on the parameter estimates. Comparisons are made with an off-line Newton type maximum likelihood algorithm which uses a state variable model representation. Results are presented from simulation random response data which quantify the identifed parameter convergence behavior for various levels of random excitation which is typical of wind tunnel turbulence levels. The RML technique is applied to hingless rotor test data from the NASA Langley Research Center Helicopter Hover Facility.

Molusis, J. A.↗
NASA NTRS2012

Upper-Ocean Heat Balance Processes and the Walker Circulation in CMIP5 Model Projections

Considerable uncertainty remains as to the importance of mechanisms governing decadal and longer variability of the Walker Circulation, its connection to the tropical climate system, and prospects for tropical climate change in the face of anthropogenic forcing. Most contemporary climate models suggest that in response to elevated CO2 and a warmer but more stratified atmosphere, the required upward mass flux in tropical convection will diminish along with the Walker component of the tropical mean circulation as well. Alternatively, there is also evidence to suggest that the shoaling and increased vertical stratification of the thermocline in the eastern Pacific will enable a muted SST increase there-- preserving or even enhancing some of the dynamical forcing for the Walker cell flow. Over the past decade there have been observational indications of an acceleration in near-surface easterlies, a strengthened Pacific zonal SST gradient, and globally-teleconnected dislocations in precipitation. But is this evidence in support of an ocean dynamical thermostat process posited to accompany anthropogenic forcing, or just residual decadal fluctuations associated with variations in warm and cold ENSO events and other stochastic forcing? From a modeling perspective we try to make headway on this question by examining zonal variations in surface energy fluxes and dynamics governing tropical upper ocean heat content evolution in the WCRP CMIP5 model projections. There is some diversity among model simulations; for example, the CCSM4 indicates net ocean warming over the IndoPacific region while the CSIRO model concentrates separate warming responses over the central Pacific and Indian Ocean regions. The models, as with observations, demonstrate strong local coupling between variations in column water vapor, downward surface longwave radiation and SST; but the spatial patterns of changes in the sign of this relationship differ among models and, for models as a whole, with observations. Our analysis focuses initially on probing the inter-model differences in energy fluxes / transports and Walker Circulation response to forcing. We then attempt to identify statistically the El Nino- / La Nina-related ocean heat content variability unique to each model and regress out the associated energy flux, ocean heat transport and Walker response on these shorter time scales for comparison to that of the anthropogenic signals.

Robertson, F. R.↗
DOE OSTI2016

The development of explosions in axisymmetric ab initio core-collapse supernova simulations of 12–25 M{sub ⊙} STARS

We present four ab initio axisymmetric core-collapse supernova simulations initiated from 12, 15, 20, and 25 M{sub ⊙} zero-age main sequence progenitors. All of the simulations yield explosions and have been evolved for at least 1.2 s after core bounce and 1 s after material first becomes unbound. These simulations were computed with our Chimera code employing RbR spectral neutrino transport, special and general relativistic transport effects, and state-of-the-art neutrino interactions. Continuing the evolution beyond 1 s after core bounce allows the explosions to develop more fully and the processes involved in powering the explosions to become more clearly evident. We compute explosion energy estimates, including the negative gravitational binding energy of the stellar envelope outside the expanding shock, of 0.34, 0.88, 0.38, and 0.70 Bethe (B ≡ 10{sup 51} erg) and increasing at 0.03, 0.15, 0.19, and 0.52 B s{sup −1}, respectively, for the 12, 15, 20, and 25 M{sub ⊙} models at the endpoint of this report. We examine the growth of the explosion energy in our models through detailed analyses of the energy sources and flows. We discuss how the explosion energies may be subject to stochastic variations as exemplfied by the effect of the explosion geometry of the 20 M{sub ⊙} model in reducing its explosion energy. We compute the proto-neutron star masses and kick velocities. We compare our results for the explosion energies and ejected {sup 56}Ni masses against some observational standards despite the large error bars in both models and observations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

On analysis and stochastic modeling of the particle kinetic energy equation in particle-laden isotropic turbulent flows

In this study, we analyze three-dimensional particle-laden, isotropic turbulence to develop an understanding of inertial particle dynamics from a kinetic energy perspective. Data trends implying inhomogeneous sampling of the flow by particles are identified and used to support a proposed particle behavior: particles appear to accumulate in regions of low flow kinetic energy over time because they lose kinetic energy and slow down in such regions, ultimately causing them to spend more time there. To elucidate this behavior, we derive a particle kinetic energy equation from the particle momentum equation, which incorporates inertial effects through the Schiller–Naumann drag correlation. Upon extracting fundamental physics from this equation, hypotheses regarding the role of the Stokes number in the temporal change of particle kinetic energy and the previously proposed particle behavior are evaluated using simulation data considering three Stokes numbers. Finally, a Fokker–Planck equation is used to derive the steady-state probability density function of the particle kinetic energy. The model fits the simulation data well and provides a tool for further investigation into understanding preferential concentration, as well as a reduced order model for predicting particle kinetic energy in turbulent flows.

42 ENGINEERING↗
DOE OSTI2017

Long-term strategic planning of inter-city fast charging infrastructure for battery electric vehicles

Here, this study introduces a multistage mixed integer modeling framework for long-term strategic planning of the battery electric vehicle (BEV) inter-city fast charging infrastructure. In response to the growing BEV inter-city travel demand, the framework integrates both an optimization model for decisions on where and when to build charging stations and a stochastic queuing model to determine how many chargers are needed for each station. A genetic algorithm based heuristic method is developed to efficiently solve the problem. The model is applied to investigate the long-term infrastructure requirement in the state of California where significant growth in BEV demand is expected in coming decades. Our findings indicate that the charging infrastructure is expanded in both network coverage (number of stations) and service capacity (number of chargers per station) as the BEV demand grows. We also found that the infrastructure requirement is dependent on many factors, such as the BEV electrified range, the required level of service at charging stations, and the range anxiety cost. For most simulated scenarios, the results show that it is beneficial to invest in the inter-city fast charging infrastructure, even though the range anxiety cost is at its low end.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Stochastic Thermo-Hydro Modeling and Neural Network Surrogate Development for Thermal Resource Assessment of the Galleries-to-Calories Geobattery

The Galleries-to-Calories Geobattery concept explores the use of abandoned coal mine workings for large-scale thermal energy transport and storage. The system involves injecting waste heat from a supercomputing facility into flooded mine galleries, where groundwater flow can store and transport thermal energy for potential recovery in downgradient district heating and cooling applications. To evaluate the feasibility and performance of the Geobattery under geological and operational uncertainty, we developed a suite of stochastic thermo-hydrological (TH) simulations using Monte Carlo sampling of key uncertain parameters (e.g., permeability, porosity, thermal conductivity, specific heat capacity) and operating conditions (e.g., injection rate, injection temperature). Results identified injection rate and temperature as the most influential parameters governing thermal front propagation, while the geometry of the room-and-pillar structure played a critical role in directing the extent and orientation of thermal advancement. Optimal combinations of material properties for maximizing heat recovery were also determined. To address the high computational cost of coupled-process stochastic modeling, we trained a neural network surrogate model on 24,000 physics-based realizations, achieving an R² > 0.99 and MAE < 0.1 for temperature predictions at monitoring locations. This surrogate enabled an additional 100,000 realizations for global sensitivity analysis and probabilistic thermal resource assessment. The integrated stochastic physics–surrogate modeling framework offers a computationally efficient tool for quantifying uncertainty, identifying key drivers, and informing early-stage design decisions for Geobattery systems.

15 - GEOTHERMAL ENERGY↗
DOE OSTI2010

Strategic planning for power system restorations

This paper considers the power system restoration planning problem (PSRPP) for disaster recovery, a fundamental problem faced by all populated areas. PSRPPs are complex stochastic optimization problems that combine resource allocation, warehouse location, and vehicle routing considerations. Furthermore, electrical power systems are complex systems whose behavior can only be determined by physics simulations. Moreover, these problems must be solved under tight runtime constraints to be practical in real-world disaster situations. This work is three fold: (1) it formalizes the specification of PSRPPs; (2) introduces a simple optimization-simulation hybridization necessary for solving PSRPPs; and (3) presents a complete restoration algorithm that utilizes the strengths of mixed integer programming, constraint programming, and large neighborhood search. This paper studied a novel problem in the field of humanitarian logistics, the Power System Restoration Problem (PSRPP). The PSRPP models the strategic planning process for post disaster power system recovery. The paper proposed a multi-stage stochastic hybrid optimization algorithm that yields high quality solutions to real-world benchmarks provided by Los Alamos National Laboratory (LANL). The algorithm uses a variety of technologies, including MIP, constraint programming, and large neighborhood search, to exploit the structure of each individual optimization subproblem. The experimental results on hurricane disaster benchmarks indicate that the algorithm is practical from a computational standpoint and produce significant improvements over existing relief delivery procedures.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Extracting forces from noisy dynamics in dusty plasmas

Extracting environmental forces from noisy data is a common yet challenging task in complex physical systems. Machine learning (ML) represents a robust approach to this problem, yet is mostly tested on simulated data with known parameters. Here we use supervised ML to extract the electrostatic, dissipative, and stochastic forces acting on micron-sized charged particles levitated in an argon plasma (dusty plasma). By tracking the sub-pixel motion of particles in subsequent images, we successfully estimated these forces from their random motion. The experiments contained important sources of non-Gaussian noise, such as drift and pixel-locking, representing a data mismatch from methods used to analyze simulated data with purely Gaussian noise. Our model was trained on simulated particle trajectories that included all of these artifacts, and used more than 100 dynamical and statistical features, resulting in a prediction with 50\% better accuracy than conventional methods. Lastly, in systems with two interacting particles, the model provided non-contact measurements of the particle charge and Debye length in the plasma environment.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2014

Deviational simulation of phonon transport in graphene ribbons with ab initio scattering

We present a deviational Monte Carlo method for solving the Boltzmann-Peierls equation with ab initio 3-phonon scattering, for temporally and spatially dependent thermal transport problems in arbitrary geometries. Phonon dispersion relations and transition rates for graphene are obtained from density functional theory calculations. The ab initio scattering operator is simulated by an energy-conserving stochastic algorithm embedded within a deviational, low-variance Monte Carlo formulation. The deviational formulation ensures that simulations are computationally feasible for arbitrarily small temperature differences, while the stochastic treatment of the scattering operator is both efficient and exhibits no timestep error. The proposed method, in which geometry and phonon-boundary scattering are explicitly treated, is extensively validated by comparison to analytical results, previous numerical solutions and experiments. It is subsequently used to generate solutions for heat transport in graphene ribbons of various geometries and evaluate the validity of some common approximations found in the literature. Our results show that modeling transport in long ribbons of finite width using the homogeneous Boltzmann equation and approximating phonon-boundary scattering using an additional homogeneous scattering rate introduces an error on the order of 10% at room temperature, with the maximum deviation reaching 30% in the middle of the transition regime.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

A Stochastic Multi-Criteria Decision-Making Algorithm for Dynamic Load Prioritization in Grid-Interactive Efficient Buildings

Increasing deployment of advanced sensing, controls, and communication infrastructure enables buildings to provide services to the power grid, leading to the concept of grid-interactive efficient buildings. Since occupant activities and preferences primarily drive the availability and operational flexibility of building devices, there is a critical need to develop occupant-centric approaches that prioritize devices for providing grid services, while maintaining the desired end-use quality of service. In this paper, we present a decision-making framework that facilitates a building owner/operator to effectively prioritize loads for curtailment service under uncertainties, while minimizing any adverse impact on the occupants. The proposed framework uses a stochastic (Markov) model to represent the probabilistic behavior of device usage from power consumption data, and a load prioritization algorithm that dynamically ranks building loads using a stochastic multi-criteria decision-making algorithm. The proposed load prioritization framework is illustrated via numerical simulations in a residential building use-case, including plug-loads, air-conditioners, and plug-in electric vehicle chargers, in the context of load curtailment as a grid service. Suitable metrics are proposed to evaluate the closed-loop performance of the proposed prioritization algorithm under various scenarios and design choices. Scalability of the proposed algorithm is established via computational analysis, while time-series plots are used for intuitive explanation of the ranking choices.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

EVI-EnSitePy (Electric Vehicle Infrastructure – Energy Estimation and Site Optimization Tool in Python) [EVI-X Modeling Suite] [SWR-25-07]

EVI-EnSitePy is a comprehensive agent-based tool designed for the analysis and design of high-power charging sites, encompassing a wide array of site agents including Electric Vehicles (EVs), chargers, energy storage units (ESS), renewable energy resources (DER), and loads. This versatile tool offers diverse functionalities and a modular modeling approach, allowing detailed configuration of agents based on power ratings, port numbers, energy capacities, demand requirements, charger interfaces, and flexibility to customize the tool for project specific goals. By simulating agent interactions and employing various metrics, EVI-EnSitePy enables the assessment of site performance, exploration of energy management systems (EMS), and implementation of innovative EV charging policies. Utilizing EV charge schedules and arrival states, the tool performs thorough charging site simulations, with outputs consisting of agent and site-level power profiles and statistical metrics. Employing a tree graph structure, EVI-EnSitePy supports nested site structures and power distribution modeling. The tool's ability to generate charging schedules deterministically or via stochastic analysis further enhances its versatility. Through its features and capabilities, EVI-EnSitePy offers a powerful platform for informed decision-making in the realm of high-power charging site design and operation.

Jackson, Derek [National Renewable Energy Laborato↗
DOE OSTI2022

GFINNs: GENERIC formalism informed neural networks for deterministic and stochastic dynamical systems

Here we propose the GENERIC formalism informed neural networks (GFINNs) that obey the symmetric degeneracy conditions of the GENERIC formalism. GFINNs comprise two modules, each of which contains two components. We model each component using a neural network whose architecture is designed to satisfy the required conditions. The component-wise architecture design provides flexible ways of leveraging available physics information into neural networks. We prove theoretically that GFINNs are sufficiently expressive to learn the underlying equations, hence establishing the universal approximation theorem. We demonstrate the performance of GFINNs in three simulation problems: gas containers exchanging heat and volume, thermoelastic double pendulum and the Langevin dynamics. In all the examples, GFINNs outperform existing methods, hence demonstrating good accuracy in predictions for both deterministic and stochastic systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Preliminary study on applying the method of characteristics to pebble-bed HTR - 18637

Neutron transport in high temperature pebble-bed gas-cooled reactor, which is a promising generation IV reactor candidate with cylindrical core, graphite moderator and spherical TRISO coated particles fuel, is of great interest. However, the stochastic method namely Monte Carlo faces with tremendous simulation of particles and the conventional two-step method featured by homogenization only can give the scalar flux. In this paper, method of characteristics is trying to be applied to pebble bed reactor because of its advantages in arbitrary geometry and the ability to obtain solution in neutronically large domains. Some challenges occur when it comes to HTR. First, the amount of calculation and the memory requirement are huge due to the large quantities of spherical fuel and stochastic distribution of pebbles. Second, the arrangement of characteristics and ray tracing process is more complex comparing to LWR with cuboid geometry because the stochastic distribution of the spherical assemblies and the longer neutron mean free path in pebble bed HTR. Third, the treatment of the multiscale sphere fuel need be detail studied to determine the subdivision of flat source region. At present, preliminary research on this subject is undergoing. This paper is concentrated on the modeling of the pebble-bed reactor core by constructive solid geometry (CSG) which allows complex spatial models to be built using boolean operations, characteristics arrangement and the treatment of boundary condition. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2013

Application of adaptive hierarchical sparse grid collocation to the uncertainty quantification of nuclear reactor simulators

Recent efforts in the application of uncertainty quantification to nuclear systems have utilized methods based on generalized perturbation theory and stochastic sampling. While these methods have proven to be effective they both have major drawbacks that may impede further progress. A relatively new approach based on spectral elements for uncertainty quantification is applied in this paper to several problems in reactor simulation. Spectral methods based on collocation attempt to couple the approximation free nature of stochastic sampling methods with the determinism of generalized perturbation theory. The specific spectral method used in this paper employs both the Smolyak algorithm and adaptivity by using Newton-Cotes collocation points along with linear hat basis functions. Using this approach, a surrogate model for the outputs of a computer code is constructed hierarchically by adaptively refining the collocation grid until the interpolant is converged to a user-defined threshold. The method inherently fits into the framework of parallel computing and allows for the extraction of meaningful statistics and data that are not within reach of stochastic sampling and generalized perturbation theory. This paper aims to demonstrate the advantages of spectral methods-especially when compared to current methods used in reactor physics for uncertainty quantification-and to illustrate their full potential. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Stochastic Ensemble Generation for Improved Characterization of Representing Geologic Variability in a Reservoir: IBDP Case Study for SMART Initiative

This document is a poster covering the findings from activities on training data generation, specifically geologic ensemble generation. The generated geologic realizations captured the range of possible permeability distributions of the subsurface at the Illinois Basin - Decatur Project (IBDP) site, based on available well log variabilities. The percentages of reservoirs and baffles in the injection zone and a truncation of baffle permeability led to more variance in the simulations. This will be used to build forward modeling, history matching, and optimization workflows. The geologic realizations were also ranked according to dynamic measures of hydraulic diffusivity, and simulations confirm a greater contrast between the reservoir and the baffles during injection.

stochastic ensemble generation↗