Process Engineering | Reservoir Technology

# Reservoirs and Applied Mathematics

Faculty of Engineering. Institute of Oil and Gas and Chemical Engineering Department. UBA

Las Heras 2214 3rd Floor. (C1127AAR) Ciudad Autónoma de Buenos Aires.

Phone: + 54 11 4514 3013/3026

## Research Field

Energy.

Applied Mathematics.

RESEARCH:

-Mathematical Modeling - Numerical simulation - Inverse Problems.

-Applications: Reservoir Engineering, CO2 sequestration and seismic exploration.

## Members

**Direction:**

**Savioli, Gabriela Beatriz. PhD.** Profesora Asociada.

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**Santos, Juan. PhD.** Investigador principal CONICET.

**Morelli, María De Los Ángeles. M.S.** Profesional Principal de CONICET. Profesora Adjunta.

**Destefanis, María Florencia. ** Tesista de grado.

**Olmi, María Belén. ** Tesista de grado.

## Work Description

The group is devoted to mathematical modeling and numerical simulation of physical processes and technological systems are described by partial differential equations.

In particular, work on the following issues:

-Reservoir Engineering. Modeling of multiphase flow equations in porous media. Development of numerical simulation of processes of primary recovery, secondary and enhanced oil. Estimation of reservoir rock properties.

-Seismic exploration. Hyperbolic system modeling wave propagation with boundary and initial conditions. Estimation of wave velocity that characterizes the environment.

-Geological sequestration of CO2. Inyeccción consists of CO2 in a geological formation (eg. Saline aquifers) to mitigate the greenhouse effect. Yet little is known about the effectiveness of CO2 storage for long periods of time. Therefore, the numerical simulation of CO2 injection and seismic monitoring is an important tool to understand and predict their behavior over time.

-Estimation of parameters. A special case of inverse problem, the parameters appear as coefficients of the equations or boundary conditions. In general are functions of spatial variables, time and / or solutions of the equation system. The most common approach to estimation is to propose a functional model that depends on a finite number of coefficients. An updated methodology is to estimate the parameters without imposing any functional model and discretize the direct model as a step towards the minimization.Thus, it solves a least squares problem in functional spaces.