Decorrelation of Poroelastic Data via Multiscale Mollifiers Wavelets

TL;DR

This paper introduces a multiscale wavelet-based method to decompose poroelastic data, enabling enhanced visualization of underlying structures and interfaces in geophysical applications like reservoir management.

Contribution

It develops physically motivated multiscale mollifier wavelets for poroelastic data decomposition, revealing details not visible in the original data.

Findings

Effective multiscale decomposition of displacement and pore pressure data.

Enhanced visualization of interfaces and structures in poroelastic datasets.

Potential applications in geophysics and reservoir analysis.

Abstract

Poroelasticity can be classified with geophysics and describes the interaction between solids deformation and the pore pressure in a porous medium. The investigation of this effect is anywhere interesting where a porous medium and a fluid come together into play, for example this is the case in geothermics. More precisely, it is an important aspect in reservoir management since the replacement of the water in the reservoir some kilometers below the Earth's surface has an effect on the surrounding material and of course displacement of the solid increases or decreases the pore pressure. The underlying physical processes are deduced with the help of linear elasticity, conservation of linear momentum, conservation of mass and Darcy's law. They result in partial differential equations, called the quasistatic equations of poroelasticity (QEP). In this paper, we want to do a multiscale decomposition of the components displacement and pore pressure. This should provide us with more information about the data that means visualize underlying structures in the different decomposition scales that cannot be seen in the whole data. The aim is to detect interfaces and extract more details of the data. For this purpose, we construct physically motivated scaling functions by mollifying the appropriate fundamental solutions. Here we have a closer look at the scaling functions fulfilling the necessary theoretical requirements of an approximate identity. The corresponding wavelets are constructed by subtraction of two consecutive scaling functions.