In the process of fracturing construction in shale gas reservoirs, microseism is a common means and effective method to evaluate the fracturing effect, but it is not suitable for large-scale and large batches due to its high applicability conditions and cost. Therefore, a concise, fast and low-cost post-fracturing effect evaluation method is needed to evaluate the complexity of fractures formed by fracturing in shale gas reservoirs. In this paper, based on the basic theory of the G-function, the free variable μ was introduced to correct the filter loss coefficient with the participation of natural fractures, and thousands of G-function curves were plotted with fracturing data from hundreds of gas wells in the southern Fuling shale gas field in China. Through the feature analysis of the curve morphology, a G-function graphic template conforming to the block was established, which contains four types and eight morphologies. Four characteristic parameters in the G-function curve were selected to establish a productivity evaluation model for shale gas wells. Based on the validation results, it can be seen that the G-function graphic template and productivity evaluation model proposed by the author had a good correlation with the post-fracturing productivity of shale gas wells, which provided a fast, economical and accurate method for the post-fracturing effect evaluation and productivity evaluation of shale gas wells and can effectively provide feedback on the field fracturing effect and guide subsequent fracturing construction.

With the rapid development of computer science and technology, the Chinese petroleum industry has ushered in the era of big data. In this study, by collecting fracturing data from 303 horizontal wells in the Fuling Shale Gas Demonstration Area in China, a series of big data analysis studies was conducted using Pearson’s correlation coefficient, the unweighted pair group with arithmetic means method, and the graphical plate method to determine which is best. The fracturing parameters were determined through a series of big data analysis studies. The big data analysis process is divided into three main steps. The first is data preprocessing to screen out eligible, high-yielding wells. The second is a fracturing parameter correlation clustering analysis to determine the reasonableness of the parameters. The third is a big data panel method analysis of specific fracturing construction parameters to determine the optimal parameter range. The analyses revealed that the current amount of 100 mesh sand in the Fuling area is unreasonable; further, there are different preferred areas for different fracturing construction parameters. We have combined different fracturing parameter schemes by preferring areas. This analysis process is expected to provide new ideas regarding fracturing scheme design for engineers working on the frontline.