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Full-Text Articles in Engineering
Prediction Model Of Coalbed Methane Content Based On Well Logging Parameter Optimization, Chen Tao, Zhang Zhansong, Zhou Xueqing, Guo Jianhong, Xiao Hang, Tan Chenyang, Qin Ruibao, Yu Jie
Prediction Model Of Coalbed Methane Content Based On Well Logging Parameter Optimization, Chen Tao, Zhang Zhansong, Zhou Xueqing, Guo Jianhong, Xiao Hang, Tan Chenyang, Qin Ruibao, Yu Jie
Coal Geology & Exploration
With the development of coalbed methane(CBM) exploration, higher accuracy of CBM content prediction is required. Based on the response characteristics of CBM logging, the correlation between logging parameters and gas content is analyzed, and the optimization strategy of logging parameters by combining MIV technology with LSSVM is proposed. The optimal logging parameters are selected as the input independent variables of network modeling, and the core parameters of LSSVM(Least Squares Support Vector Machine) network are optimized by particle swarm optimization. Finally, a set of MIV-PSO-LSSVM model suitable for CBM content prediction is constructed. The prediction performances of LSSVM, PSO-LSSVM, MIV-LSSVM, MIV-PSO-LSSVM …
Prediction Of Coal Seam Gas Content Based On Abc-Bp Model, Zang Zijing, Wu Haibo, Zhang Pingsong, Dong Shouhua
Prediction Of Coal Seam Gas Content Based On Abc-Bp Model, Zang Zijing, Wu Haibo, Zhang Pingsong, Dong Shouhua
Coal Geology & Exploration
It is valuable to exploit the coalbed-methane which is rich in our country. The prediction of gas content in coalbed methane reservoir is a key step in the early stage of development and utilization. In recent years, BP neural network algorithm has been often used in coalbed methane prediction, but the model has some shortcomings in the training process, such as slow convergence speed, sensitive to initial value and easy to fall into local minimum value. Therefore, this paper proposed an improved BP neural network prediction model characterized by artificial bee colony algorithm. Firstly, R-type cluster analysis was used to …