# On anisotropy parameters and fluid detection in equivalent TI medium

## TL;DR

This paper evaluates anisotropy parameters in transversely isotropic media derived from layered isotropic rocks to develop a new fluid detection method based on the anisotropy parameter 4.

## Contribution

The study introduces a novel fluid detection approach using anisotropy parameters , , and , validated through Monte Carlo simulations and cross-plot analysis.

## Key findings

- Identified eleven effective fluid indicators.
- Demonstrated the relationship between anisotropy parameters and fluid content.
- Provided a new pattern for fluid detection in TI media.

## Abstract

We consider a long-wave transversely isotropic (TI) medium equivalent to a series of finely parallel-layered isotropic layers, obtained using the \citet{Backus} average. In such a TI equivalent medium, we verify the \citet{Berrymanetal} method of indicating fluids and the author's method \citep{Adamus}, using anisotropy parameter $\varphi$. Both methods are based on detecting variations of the Lam\'e parameter, $\lambda$, in a series of thin isotropic layers, and we treat these variations as potential change of the fluid content. To verify these methods, we use Monte Carlo (MC) simulations; for certain range of Lam\'e parameters $\lambda$ and $\mu$---relevant to particular type of rocks---we generate numerous combinations of these parameters in thin layers and, after the averaging process, we obtain their TI media counterparts. Subsequently, for each of the aforementioned media, we compute $\varphi$ and \citet{Thomsen} parameters $\epsilon$ and $\delta$. We exhibit $\varphi$, $\epsilon$ and $\delta$ in a form of cross-plots and distributions that are relevant to chosen range of $\lambda$ and $\mu$. We repeat that process for various ranges of Lam\'e parameters. Additionally, to support the MC simulations, we consider several numerical examples of growing $\lambda$, by using scale factors. As a result of the thorough analysis of the relations among $\varphi$, $\epsilon$ and $\delta$, we find eleven fluid detectors that compose a new fluid detection method. Based on these detectors, we show the quantified pattern of indicating change of the fluid content.