Convergence of single rate and multirate undrained split iterative schemes for a fractured biot model

Almani, Tameem; Kumar, Kundan

doi:10.1007/s10596-021-10119-1

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Cited by 5 publications

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Natural Fracture Network Model Using Machine Learning Approach

Merembayev

2023

Lecture Notes in Computer Science

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A fracture network model is a powerful tool for characterizing fractured rock systems. In this paper, we present the fracture network model by integrating a machine learning algorithm in two-dimensional setting to predict the natural fracture topology in porous media. We also use a machine learning algorithm to predict the fracture azimuth angle for the natural fault data from Kazakhstan. The results indicate that the fracture network model with LightGBM performs better in designing a fracture network parameter for hidden areas based on data from the known area. In addition, the numerical result of the machine learning algorithm shows a good result for randomly selected data of the fracture azimuth.

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