Application of artificial intelligence technology in unconventional natural gas production forecasting

Improving the speed and accuracy of unconventional natural gas production forecasts is the key to scientific and efficient development of unconventional resources. The existing prediction methods based on the transmission mechanism make assumptions and simplifications on the model, and it is difficult to comprehensively and accurately evaluate the main control factors of production capacity, resulting in large production prediction errors. This paper proposes a productivity prediction method for unconventional natural gas wells based on artificial intelligence (AI) and data mining technologies. We use the Pearson correlation coefficient and grey relational analysis to screen out the main control factors, and select the best yield intelligent forecasting model by training and comparing a variety of commonly used machine learning methods. This paper takes the Alberta tight gas field in Canada as an example to illustrate the effectiveness and practicability of this method.