What question did this study set out to answer?

This research aims to develop a robust data-driven method for estimating permeability from capillary pressure data in various rock types.

July 12, 2026Computational Geosciences0 citationsOpen Access

A new data-driven approach for estimating permeability by integrating capillary pressure analytics with big data

OROriyomi RaheemThe University of Texas at Austin MMMisael M. MoralesThe University of Texas at Austin WSWardana SaputraThe University of Texas at Austin

Key Points

This research aims to develop a robust data-driven method for estimating permeability from capillary pressure data in various rock types.
Analyzed 199 samples across 7 hydrocarbon fields using MICP and routine core analysis data.
Employed machine-learning models including ridge regression, random forests, and artificial neural networks (ANNs) to predict permeability.
Utilized generalized extreme-value CDF-PDF fitting and Gaussian Mixture Modeling to enhance accuracy.
GEV CDF-PDF method correlated strongly with permeability, achieving R² of 95%.
Ridge regression and random forests improved accuracy to 81%, while ANNs reached 89%.
Swanson’s method consistently overestimated permeability in tight rocks.

Abstract

Permeability estimation from mercury injection capillary pressure (MICP) data remains challenging in heterogeneous and low-permeability rocks because conventional empirical methods often rely on single-point pore-throat descriptors. This study presents a data-driven workflow that integrates generalized extreme-value (GEV) CDF-PDF fitting, petrophysical rock classification, and machine-learning models to estimate permeability from MICP-derived parameters and porosity. We analyzed 199 samples from 7 hydrocarbon fields, incorporating MICP and routine core analysis (RCA) data spanning marine mud, tight gas, carbonate, and sandstone formations. Parametric fitting was performed using Thomeer’s method, Gaussian cumulative distribution and its probability distribution function (CDF-PDF), and generalized extreme-value (GEV) CDF-PDF. Optimized fitting parameters from GEV, including location (μ), scale (σ gev ), entry pressure (P e ) equivalent and weighting constraints, served as inputs for machine-learning methods to estimate permeability. Ridge regression, random forests, and artificial neural networks (ANNs) were applied to estimate logarithmic permeability from porosity and optimized parameters and benchmarked against Swanson’s method. The GEV CDF-PDF method achieved the strongest correlation with permeability, with an R 2 of 95%. Incorporating Gaussian Mixture Modeling (GMM) with k-Means + + initialization enhanced the accuracy of the estimation. Ridge regression and random forests improved to 81%, while ANNs achieved 89%. In contrast, Swanson’s method consistently overestimated permeability in tight rocks. The universal method developed in this paper exhibits robust applicability and computational efficiency, yielding permeability estimations in under one minute of CPU time on standard hardware. Nonlinear machine-learning methods with regularization and ANN, combined with prior classification, are recommended for improved accuracy. Ensuring diverse training datasets and high-quality MICP and RCA data is essential to maximize reliability of results as well as accurate permeability estimation across varied rock types.

问 AI

Bookmark

View Full Paper