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

2025-03-21

AI Summary

Accurate prediction of microporosity and permeability in clastic reservoirs remains a critical challenge in reservoir quality evaluation—particularly where high-cost laboratory characterization (e.g., mercury injection capillary pressure, scanning electron microscopy) is impractical. To address this, we propose an uncertainty-driven stochastic forest modeling framework that integrates readily available field and basic lab data—including bulk porosity, grain-size distribution, and spectral gamma-ray (SGR) measurements—for low-cost, high-accuracy prediction. Our key innovation lies in embedding uncertainty propagation analysis directly into the random forest training pipeline: geological parameter variability is quantified and used to augment the training dataset, thereby enhancing model robustness and generalizability. Experimental validation demonstrates prediction accuracies of 93% for microporosity and 88% for permeability, substantially reducing reliance on expensive petrophysical characterization. This work establishes a transferable machine learning paradigm for rapid, cost-effective reservoir assessment.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Stochastic Optimization
• Data Mining & Knowledge Management: Knowledge Acquisition from the Web

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

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.

Problem

• Predict microporosity and permeability in clastic reservoirs
• Reduce reliance on expensive lab methods like MICP and SEM
• Enhance reservoir quality assessment using machine learning

Innovation

• Random Forest predicts microporosity and permeability
• Uses porosity, grain size, and gamma-ray data
• Integrates uncertainty analysis for robust predictions