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<p>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<sub>gev</sub>), entry pressure (P<sub>e</sub>) 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<sup>2</sup> 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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月10日

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  • Oriyomi Raheem,
  • Misael M. Morales,
  • Wardana Saputra,
  • Carlos Torres-Verdín,
  • Craig Phillips,
  • Chicheng Xu

摘要

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 (σgev), entry pressure (Pe) 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 R2 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.