Abstract: To address the problem that the main controlling factors of single shale gas well production in Weiyuan Block, Sichuan Basin are unknown, based on the geological and engineering data and production data of 132 gas wells which have been put in production for more than a year in the area, an analytical study was conducted by the gray correlation method. The study shows that, the main controlling factors affecting the first-year cumulative production of a single shale gas well are proppant dose, number of fracturing stages, median vertical depth of horizontal wells, fracturing section length, fracturing fluid volume, porosity, pressure coefficient and sanding intensity. It was clear that the machine learning method was higher in accuracy after comparison of the machine learning method and the traditional empirical formula method to predict the first year's cumulative output and initial output. Meanwhile, based on the analysis of the main controlling factors, the machine learning method applicable to the study area was preferably selected as the support vector machine method, and its prediction accuracy was higher than 90%. The study has an important implication to the productivity evaluation of similar shale gas blocks.

Key words: Shale gas, main controlling factor, single-well production, machine learning, support vector machine, BP neural network, Sichuan Basin