Imaging of a fluid injection process using geophysical data - A didactic example

In many subsurface industrial applications, fluids are injected into or withdrawn from a geologic formation. It is of practical interest to quantify precisely where, when, and by how much the injected fluid alters the state of the subsurface. Routine geophysical monitoring of such processes attempts to image the way that geophysical properties, such as seismic velocities or electrical conductivity, change through time and space and to then make qualitative inferences as to where the injected fluid has migrated. The more rigorous formulation of the time-lapse geophysical inverse problem forecasts how the subsurface evolves during the course of a fluid-injection application. Using time-lapse geophysical signals as the data to be matched, the model unknowns to be estimated are the multiphysics forward-modeling parameters controlling the fluid-injection process. Properly reproducing the geophysical signature of the flow process, subsequent simulations can predict the fluid migration and alteration in the subsurface. The dynamic nature of fluid-injection processes renders imaging problems more complex than conventional geophysical imaging for static targets. This work intents to clarify the related hydrogeophysical parameter estimation concepts

Using parametric model order reduction for inverse analysis of large nonlinear cardiac simulations

Predictive high-fidelity finite element simulations of human cardiac mechanics commonly require a large number of structural degrees of freedom. Additionally, these models are often coupled with lumped-parameter models of hemodynamics. High computational demands, however, slow down model calibration and therefore limit the use of cardiac simulations in clinical practice. As cardiac models rely on several patient-specific parameters, just one solution corresponding to one specific parameter set does not at all meet clinical demands. Moreover, while solving the nonlinear problem, 90% of the computation time is spent solving linear systems of equations. We propose to reduce the structural dimension of a monolithically coupled structure-Windkessel system by projection onto a lower-dimensional subspace. We obtain a good approximation of the displacement field as well as of key scalar cardiac outputs even with very few reduced degrees of freedom, while achieving considerable speedups. For subspace generation, we use proper orthogonal decomposition of displacement snapshots. Following a brief comparison of subspace interpolation methods, we demonstrate how projection-based model order reduction can be easily integrated into a gradient-based optimization. We demonstrate the performance of our method in a real-world multivariate inverse analysis scenario. Using the presented projection-based model order reduction approach can significantly speed up model personalization and could be used for many-query tasks in a clinical setting

Core-scale solute transport model selection using Monte Carlo analysis

Model applicability to core-scale solute transport is evaluated using breakthrough data from column experiments conducted with conservative tracers tritium and sodium-22 , and the retarding solute uranium-232 . The three models considered are single-porosity, double-porosity with single-rate mobile-immobile mass-exchange, and the multirate model, which is a deterministic model that admits the statistics of a random mobile-immobile mass-exchange rate coefficient. The experiments were conducted on intact Culebra Dolomite core samples. Previously, data were analyzed using single-porosity and double-porosity models although the Culebra Dolomite is known to possess multiple types and scales of porosity, and to exhibit multirate mobile-immobile-domain mass transfer characteristics at field scale. The data are reanalyzed here and null-space Monte Carlo analysis is used to facilitate objective model selection. Prediction (or residual) bias is adopted as a measure of the model structural error. The analysis clearly shows single-porosity and double-porosity models are structurally deficient, yielding late-time residual bias that grows with time. On the other hand, the multirate model yields unbiased predictions consistent with the late-time slope diagnostic of multirate mass transfer. The analysis indicates the multirate model is better suited to describing core-scale solute breakthrough in the Culebra Dolomite than the other two models

Algoritmo de simulación de Dymola en Matlab para investigación de modelos de usuario deterministas en su aplicación a edificios

Los modelos de representación de edificios son una herramienta importante para simular su comportamiento dinámico. La creación de dicho modelo requiere una gran labor de esfuerzo y tiempo, pero el objetivo principal es el estudio de la influencia de los parámetros sobre el edificio para su control o mejora. Hasta el momento se han utilizado los perfiles de usuario aplicando lo establecido en la norma DIN 18599 y SIA 2014 para estimar las cargas internas del edificio debidas a personas, máquinas y luz y con ellas se simula el comportamiento energético del edificio. Este proyecto aborda la estimación de los parámetros debido a cargas internas a partir de medidas reales de energía consumida de calefacción. La optimización consiste en un proceso iterativo de calibración del resultado de energía de calefacción de la simulación del modelo con datos reales de consumo. Este proceso se realiza mediante tres métodos. El primer método corresponde a una función implementada dentro del programa de simu- lación Dymola. El segundo método es un programa desarrollado con Matlab que permite acoplar Dymola para la optimización de parámetros. El tercero consiste en el desarrollo de un algoritmo de Matlab que implementa el método matemático de Gauss-Newton por pasos. Tras el desarrollo y aplicación de los métodos correspondientes se estudia su comportamien- to sobre el modelo del edificio de referencia. El estudio sobre uno o varios parámetros del modelo de usuario permite obtener los resultados del proyecto y compararlos. Mediante el análisis de sensibilidad se estudia la dependencia entre parámetros del modelo de usuario y gracias a los resultados se concluye que los dos programas desarrollados con Matlab conver- gen con la solución óptima en menos tiempo y con menos iteraciones que Dymola. El proyecto se ha desarrollado en el Centro de Investigación E.ON para el Instituto de Eficien- cia Energética en Edificios y Clima Interior de Aachen (Alemania) en el ámbito del desarrollo de una herramienta de planificación integral de energía para un campus de Alemania. El pre- sente proyecto puede ayudar en la estimación de parámetros de los edificios estudiados