Permeability Prediction of Nanoscale Porous Materials Using Discrete Cosine Transform-Based Artificial Neural Networks

Author:

Li Dongshuang1,You Shaohua1ORCID,Liao Qinzhuo1ORCID,Lei Gang2,Liu Xu3,Chen Weiqing3ORCID,Li Huijian3,Liu Bo4,Guo Xiaoxi5

Affiliation:

1. College of Petroleum Engineering, China University of Petroleum-Beijing, Beijing 102249, China

2. Faculty of Engineering, China University of Geosciences, Wuhan 430074, China

3. College of Petroleum Engineering & Geosciences, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia

4. Electrical Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia

5. State Grid Information & Telecommunication Branch, Beijing 100761, China

Abstract

The permeability of porous materials determines the fluid flow rate and aids in the prediction of their mechanical properties. This study developed a novel approach that combines the discrete cosine transform (DCT) and artificial neural networks (ANN) for permeability analysis and prediction in digital rock images, focusing on nanoscale porous materials in shale formations. The DCT effectively captured the morphology and spatial distribution of material structure at the nanoscale and enhanced the computational efficiency, which was crucial for handling the complexity and high dimensionality of the digital rock images. The ANN model, trained using the Levenberg–Marquardt algorithm, preserved essential features and demonstrated exceptional accuracy for permeability prediction from the DCT-processed rock images. Our approach offers versatility and efficiency in handling diverse rock samples, from nanoscale shale to microscale sandstone. This work contributes to the comprehension and exploitation of unconventional resources, especially those preserved in nanoscale pore structures.

Funder

Science Foundation of China University of Petroleum

Publisher

MDPI AG

Subject

General Materials Science

Reference45 articles.

1. Digital rocks: A review;Jackson;Phys. Chem. Earth Parts A/B/C,2003

2. Wolf, K.H., Arfelli, F., and Speller, R.D. (2008). Handbook of Imaging Materials, Springer.

3. X-ray imaging and analysis techniques for quantifying pore-scale structure and processes in subsurface porous medium systems;Wildenschild;Adv. Water Resour.,2013

4. Recent advances in micro-CT imaging for characterizing porous media: A review;Jiang;J. Hydrol.,2023

5. A new mechanistic model for conductivity of hydraulic fractures with proppants embedment and compaction;Lei;J. Hydrology.,2021

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