Porosity and Permeability Estimations from X-Ray Tomography Images and Data Using a Deep Learning Approach
Herrera Otero, Edwar Hernando (Universidad Industrial de Santander (Colòmbia))
Oms, Oriol (Universitat Autònoma de Barcelona. Departament de Geologia)
Remacha, E. (Universitat Autònoma de Barcelona. Departament de Geologia)

Fecha:	2026
Resumen:	This work presents a novel deep learning workflow for estimating porosity and permeability from combined data, where numerical variables such as high-resolution bulk density (RHOB) and photoelectric factor (PEF) data are integrated with X-ray computed tomography (X-CT) image data, using a dual-energy X-CT approach (DECT). Convolutional neural networks (CNNs) were calibrated with routine core analysis (RCAL) laboratory measurements from one well from Sinú-San Jacinto Basin (Colombia). The CNN architecture combines two main branches: An image branch, in which a CNN extracts spatial features from normalized X-CT sections using 3 × 3 convolution layers, ReLU activation, batch normalization, and maxPooling, and a numerical branch, which processes the input vectors corresponding to RHOB and PEF using fully connected dense layers and dropout regularization. Both branches are concatenated in a fusion layer, from which the model's final predictions are made. Results indicate a strong correlation between porosity, permeability, RHOB and PEF logs, and CT images. The porosity model achieved excellent predictive performance, with an R = 0. 996, MAE = 3. 96 × 10, MSE = 3. 82 × 10, and 0. 064 maximum error. The permeability model also performed well, with a linear R = 0. 983, though metrics reflected the wide dynamic range of permeability. Consequently, artificial neural networks (ANNs) can accurately predict porosity and permeability at various depths where no corresponding laboratory data exists, demonstrating excellent predictive capabilities over several rock intervals, in a high vertical resolution because of X-CT data scale (0. 625 mm).
Derechos:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Materia:	X-ray computed tomography (X-CT) ; Artificial intelligence (AI) ; Rock images ; Well logs ; Convolutional neural network (CNN)
Publicado en:	Applied sciences (Basel), Vol. 16, Num. 3 (February 2026) , art. 1613, ISSN 2076-3417

DOI: 10.3390/app16031613

26 p, 4.7 MB

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