Abstract
My country’s coal seam permeability is generally low, and it is difficult to carry out large-scale development and utilization. For different coal seam blocks, the use of abundant field data to predict gas production can not only provide effective guidance for on-site construction, but also significantly save development costs. This paper presents a prediction model of coalbed methane production based on radial basis function network. According to the field data of X area, the correlation analysis of the factors affecting the gas production is carried out, and the main control factors with significant correlation are selected. Then, taking these main control factors as input and gas production as output, a series of radial basis function networks with different precisions were constructed through trial calculation of different expansion coefficients. Finally, with the goal of maximizing precision, a highly accurate fitting was obtained. The optimal network with good prediction effect.
Highlights
According to the internationally accepted development criteria, the most suitable development permeability of CBM is (3~4)×10-3um2 and cannot be lower than 1×103um2 [1], and the coal field with the largest permeability in my country is only (0.54~3.8)×10-3um2, which brings severe challenges to the mining of CBM in my country
radial basis function network (RBF) network has the characteristics of small amount of calculation, high precision, flexible node, simple format, etc., and has been widely used in many fields
Chen Lianjun compared the training and learning of the two networks in the literature [2], and the result is that the RBF network prediction model has fewer learning times and faster calculations
Summary
According to the internationally accepted development criteria, the most suitable development permeability of CBM is (3~4)×10-3um and cannot be lower than 1×103um2 [1], and the coal field with the largest permeability in my country is only (0.54~3.8)×10-3um, which brings severe challenges to the mining of CBM in my country. After the development of many scholars, a new method of neural network learning is derived, namely, radial basis function network (RBF). Chen Lianjun compared the training and learning of the two networks in the literature [2], and the result is that the RBF network prediction model has fewer learning times and faster calculations. It effectively avoids the tedious calculation of BP network. With the average daily gas production of a single well as the goal, the RBF network was built and predicted using the selected main control factors, which provided guidance for the targeted development of coalbed methane on-site
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