Abstract
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.
Highlights
Introduction iationsMultistage fracturing of horizontal wells is widely used in the exploration and development of shale gas
Through the method established in this paper, the shale gas flowThrough the method established in this paper, the shale gas flowback curve containing back curve containing complex flow mechanism be abstracted into simple charactercomplex flow mechanism can be abstracted intocan simple characteristic parameters and characteristic coefficients, and the relationship data and dynamic data is istic parameters and characteristic coefficients,between and the static relationship between static data established, which can help to establish a machine learning method forlearning predicting the and dynamic data is established, which can help to establish a machine method flowback curvethe of shale gas horizontal wells
The Weiyuan shale gas block was considered as a case study
Summary
The time-dependent daily flowback volume curve for the Weiyuan shale gas block in southern Sichuan Province, China, shows that the daily flowback volume is large during the early stages of continuous production and decreases to a stable value during the late stage of production. The daily flowback volume during early-stage production is two to three orders of magnitude higher than that during the later stage, and the relationship between daily flowback and time is generally exponential. The flowback of a fracturing fluid after large-scale fracturing is essentially the seepage of fluid from the stimulated reservoir volume (SRV) area to the wellbore. The corresponding law can be obtained from the seepage equation of fracturing fluid
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