Abstract：

The mechanism of the factors affecting the flowback ratio after fracturing in the Longmaxi Formation in the Sichuan Basin is unclear. The mechanisms by which the flowback ratio affects the productive capacity are also unclear and increasing single well production is difficult. For this purpose, based on geological data, production data and construction data, this study selects Poisson's ratio, clay content, total organic carbon content (TOC), gas content, porosity, brittleness index, formation pressure,and layer thickness of the WH block of the Longmaxi Formation in the Sichuan Basin as the geological factors. The length of the horizontal section, the length of the fracturing section, the amount of liquid used for the main fracturing, the strength of the sand reinforcement, the construction displacement, and the supporting dose are selected as the engineering factors. By establishing a proper BP neural network model, the weights of these factors affecting the prediction of shale gas well flowback ratio are analyzed. It is found that Poisson's ratio, porosity, clay content, the length of the fracturing section, the strength of the sand reinforcement, and the construction displacement are the main controlling factors in geology and engineering. For the main controlling factors, a multivariate nonlinear fitting is applied to establish a prediction template with the engineering index as the response value based on the relationship between the flowback ratio and the geological comprehensive index, and a template of the relationship between the flowback ratio and the productive capacity with the comprehensive index as the response value is further established. The analysis shows that it is necessary to pay attention to the impact of comprehensive factors on the flowback ratio and productive capacity. According to the statistics of production data, it is found that the shale gas well in the Sichuan Basin has an optimum flowback ratio, and the optimum flowback ratio can maximize the gas production of the shale gas well. The flowback ratio prediction template established in this study can effectively predict and optimize the flowback ratio,thereby helping to improve the production of shale gas wells.

Key words：Sichuan Basin; flowback ratio; Back Propagation (BP) neural network; the optimum flowback ratio; multi-factor analysis; quantitative forecast

Received： 2018-10-17

Corresponding Authors： linb_cupb@163.com

Cite this article：GUO Jiancheng, LIN Botao, XIANG Jianhua, ZHONG Hua. Study of factors affecting the flowback ratio and productive capacity of Longmaxi Formation shale in the Sichuan basin after fracturing. Petroleum Science Bulletin, 2019, 03: 273-287.

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