Intelligent coal reservoir permeability prediction method based on particle swarm optimization

Abstract

According to the coal reservoir permeability prediction method, surrounding rock stress, gas pressure, temperature and compressive strength serve as input values, permeability serves as an output value, and data are divided into a training set and a test set after being cleaned; a multiple linear regression model, a BP neural network model and an SVM model are established by using the training set, and the test set is predicted; and then a joint prediction model is established, and the most suitable weight is obtained by using a particle swarm algorithm. According to the method, multiple machine learning models are combined, the advantages of all the models are extracted, a more accurate combined prediction model is obtained, prediction deviation is reduced, the combined prediction model has good robustness, and even if deviation occurs due to model assumption, only small influence can be generated on algorithm performance. No matter how the index number and the sample number of the data change, the permeability can be accurately predicted; and the requirement for the training data volume is loose, and a precise prediction model can be obtained through small sample data training.

Description

technical field [0001] The invention relates to the field of mining engineering, in particular to a model for intelligently predicting the rock permeability of coal reservoirs, which is composed of multiple machine learning algorithms through particle swarm algorithm. Background technique [0002] During the coal mining operation, the surrounding rock stress, gas pressure, self-temperature and compressive strength of the coal reservoir are constantly changing, and these factors will lead to changes in the permeability properties of the coal rock mass, resulting in Large-scale gas gushes out, causing gas disaster accidents. Therefore, timely and accurate prediction of the permeability of coal and rock mass is of great significance for the prevention of gas disasters. At the same time, the prevention and control of coal-rock mass permeation, the drainage of coalbed methane in the coal seam and the industrialized gas extraction all require the knowledge of the permeability of ...

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Problems solved by technology

However, the accuracy and generalization of the prediction model thus established are poor, and it is extremely dependent on the number of samples. When the training sample size is small, the model may have a large error

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