# Evaluation of mineralogy per geological layers by Approximate Bayesian Computation

## TL;DR

This paper introduces a Bayesian-based method using Approximate Bayesian Computation and clustering to infer mineralogy across geological layers from wellbore logs, providing confidence estimates for each hypothesis.

## Contribution

It presents a novel mineralogic inversion approach combining ABC, clustering, and direct inversion to explore multiple hypotheses with confidence levels.

## Key findings

- Effective on synthetic datasets
- Validated on real wellbore data
- Provides confidence estimates for mineralogical hypotheses

## Abstract

We propose a new methodology to perform mineralogic inversion from wellbore logs based on a Bayesian linear regression model. Our method essentially relies on three steps. The first step makes use of Approximate Bayesian Computation (ABC) and selects from the Bayesian generator a set of candidates-volumes corresponding closely to the wellbore data responses. The second step gathers these candidates through a density-based clustering algorithm. A mineral scenario is assigned to each cluster through direct mineralogical inversion, and we provide a confidence estimate for each lithological hypothesis. The advantage of this approach is to explore all possible mineralogy hypotheses that match the wellbore data. This pipeline is tested on both synthetic and real datasets.