In the fields of geological engineering and oil - gas exploration, accurate prediction of the porosity and permeability of rocks is extremely crucial. Currently, machine learning has demonstrated great power and provided new solutions to this difficult problem. The research in the article "Prediction of Porosity and Permeability Alteration Based on Machine Learning Algorithms" aims to explore the applicability of different machine - learning algorithms to the prediction of rock properties.
The research carried out tests and analyses on more than 100 salt - bearing core samples for over 10 characteristics (such as salt concentration, initial porosity and permeability, sample depth, etc.), and established a prediction model by combining conventional core analysis data and other geological parameters. The experiment utilized a variety of machine - learning algorithms, including linear regression (with L1 and L2 regularization), decision trees, random forests, gradient boosting, neural networks and support vector machines (SVM), etc.
Among them, the double - hidden - layer neural network performed the best when predicting the three rock properties of porosity, permeability and salt concentration, fully demonstrating its strong fitting and learning capabilities. The research results show that there is a significant correlation between the changes in porosity and permeability and salt concentration. Linear regression, support vector machines and neural networks perform well in the prediction of porosity and permeability, with R² values all higher than 0.8, indicating that these models can relatively accurately capture the changing trends of porosity and permeability. However, the prediction model for salt concentration is relatively weak, with an R² value close to 0.66, but still has a certain predictive ability.
This research conclusion highlights the importance of machine learning in geological engineering. It reduces the dependence on expensive laboratory analyses and provides an effective way to predict rock properties when there is a lack of abundant experimental data. In the future, the scope of the data set can be further expanded to verify the accuracy of machine - learning methods under different geological conditions.
At the same time, other machine - learning algorithms and data pre - processing techniques can be continuously explored to improve the performance of the prediction model. With the continuous development of technology, machine learning will play an increasingly important role in geological engineering and oil - gas exploration, helping us better understand and develop underground resources, making the prediction of porosity and permeability no longer difficult and injecting new vitality into the development of the industry.