# Uncertainty-Driven Modeling of Microporosity and Permeability in Clastic Reservoirs Using Random Forest

arXiv: https://arxiv.org/abs/2503.16957 · HTML: https://mlsanity.com/paper/2503.16957/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 70 references that are in the map.
- First posted on arXiv 21 Mar 2025.

## Builds on (most cited first, 5 of 5)

- [Hyperparameters and Tuning Strategies for Random Forest](https://mlsanity.com/paper/1804.03515/) (2018)
- [Is K-fold cross validation the best model selection method for Machine Learning?](https://mlsanity.com/paper/2401.16407/) (2024)
- [Towards Better Shale Gas Production Forecasting Using Transfer Learning](https://mlsanity.com/paper/2106.11051/) (2021)
- [Direct Estimation of Porosity from Seismic Data using Rock and Wave Physics Informed Neural Networks (RW-PINN)](https://mlsanity.com/paper/2210.00042/) (2022)
- [Understanding overfitting in random forest for probability estimation: a visualization and simulation study](https://mlsanity.com/paper/2402.18612/) (2024)

## Abstract (arXiv)

Predicting microporosity and permeability in clastic reservoirs is a challenge in reservoir quality assessment, especially in formations where direct measurements are difficult or expensive. These reservoir properties are fundamental in determining a reservoir's capacity for fluid storage and transmission, yet conventional methods for evaluating them, such as Mercury Injection Capillary Pressure (MICP) and Scanning Electron Microscopy (SEM), are resource-intensive. The aim of this study is to develop a cost-effective machine learning model to predict complex reservoir properties using readily available field data and basic laboratory analyses. A Random Forest classifier was employed, utilizing key geological parameters such as porosity, grain size distribution, and spectral gamma-ray (SGR) measurements. An uncertainty analysis was applied to account for natural variability, expanding the dataset, and enhancing the model's robustness. The model achieved a high level of accuracy in predicting microporosity (93%) and permeability levels (88%). By using easily obtainable data, this model reduces the reliance on expensive laboratory methods, making it a valuable tool for early-stage exploration, especially in remote or offshore environments. The integration of machine learning with uncertainty analysis provides a reliable and cost-effective approach for evaluating key reservoir properties in siliciclastic formations. This model offers a practical solution to improve reservoir quality assessments, enabling more informed decision-making and optimizing exploration efforts.