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

• URL: http://arxiv.org/abs/2503.16957v1
• Date: Fri, 21 Mar 2025 09:05:04 GMT
• Title: Uncertainty-Driven Modeling of Microporosity and Permeability in Clastic Reservoirs Using Random Forest
• Authors: Muhammad Risha, Mohamed Elsaadany, Paul Liu,
• Abstract summary: The aim of this study is to develop a cost-effective machine learning model to predict complex reservoir properties.<n>The model achieved a high level of accuracy in predicting microporosity (93%) and permeability levels (88%).
• Score: 3.4137115855910762
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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