Production Dynamic of Coal-bed Methane After Well Pressure Based on Multi-layer Perceptron Model Inversion Study

Abstract

The inversion of production performance after fracturing of coal-bed methane well is the key technology to realize the efficient development of gas reservoir. In order to improve the inversion efficiency of traditional numerical simulation methods, with the help of machine learning modeling technology and intelligent algorithm, this paper studies the automatic inversion and programmed design of key parameters such as coal-bed methane reservoir matrix permeability, gas saturation, fracture half length, fracture number and fracture conductivity. The multi-layer perceptron model is constructed with the training data generated by the nested discrete fracture coal-bed methane numerical simulator, and the collaborative inversion of reservoir-fracture parameters is realized by combining the intelligent algorithm. The results show that: (1) Using a small number of training samples (only 100 simulated samples are required for this case study), the machine learning model can accurately simulate the relationship between fracture/reservoir parameters and daily and cumulative gas production of shale gas wells; (2) The intelligent inversion algorithm based on machine learning agent assistance has high convergence efficiency and can quickly obtain a reasonable reservoir fracture parameter combination model with high inversion accuracy. It is concluded that the combination of machine learning modeling technology and intelligent inversion algorithm is helpful to promote the application and development of intelligent optimization technology of tight gas reservoirs, and provide theoretical guidance and technical support for accelerating the intelligent development process of unconventional oil and gas reservoirs in China.

Keywords

• coal-bed methane
• multi-fractured horizontal well
• machine-learning modeling
• intelligent inversion algorithm.