Investigation of Flowback Behaviours in Hydraulically Fractured Shale Gas Well Based on Physical Driven Method

Due to the complex microscope pore structure of shale, large-scale hydraulic fracturing is required to achieve effective development, resulting in a very complicated fracturing fluid flowback characteristics. The flowback volume is time-dependent, whereas other relevant parameters, such as the permeability, porosity, and fracture half-length, are static. Thus, it is very difficult to build an end-to-end model to predict the time-dependent flowback curves using static parameters from a machine learning perspective. In order to simplify the time-dependent flowback curve into simple parameters and serve as the target parameter of big data analysis and flowback influencing factor analysis, this paper abstracted the flowback curve into two characteristic parameters, the daily flowback volume coefficient and the flowback decreasing coefficient, based on the analytical solution of the seepage equation of multistage fractured horizontal Wells. Taking the dynamic flowback data of 214 shale gas horizontal wells in Weiyuan shale gas block as a study case, the characteristic parameters of the flowback curves were obtained by exponential curve fittings. The analysis results showed that there is a positive correlation between the characteristic parameters which present the characteristics of right-skewed distribution. The calculation formula of the characteristic flowback coefficient representing the flowback potential was established. The correlations between characteristic flowback coefficient and geological and engineering parameters of 214 horizontal wells were studied by spearman correlation coefficient analysis method. The results showed that the characteristic flowback coefficient has a negative correlation with the thickness × drilling length of the high-quality reservoir, the fracturing stage interval, the number of fracturing stages, and the brittle minerals content. Through the method established in this paper, the shale gas flowback curve containing complex flow mechanism can be abstracted into simple characteristic parameters and characteristic coefficients, and the relationship between static data and dynamic data is established, which can help to establish a machine learning method for predicting the flowback curve of shale gas horizontal wells.