An unsupervised cluster model of formation fracability based on drilllog data and its application to fracture optimization

Reservoir fracability evaluation is one of the prerequisites to improve the effect of balanced fracturing of unconventional oil and gas fields. At present, reservoir fracability evaluation mainly depends on logging data theory to explain rock mechanics parameters, and the application effect on fracturing is uneven. In this paper, the characteristics of rock mechanical parameters are directly reflected by the bit rock breaking data and the reservoir fracability is clustered by drilling and logging data. We established a reservoir fracability clustering model based on a self-organizing map (SOM) unsupervised clustering algorithm. The elbow method is used to determine the optimal clustering number, and the parameter optimization method of fracture placement is formed. The optimal design of three-cluster perforation placement is carried out for typical vertical wells in the Tarim Basin with large thickness reservoirs. The results show that the drilling time, dc-exponent, weight on bit, torque, true formation resistivity, acoustic and neutron data are significantly correlated with reservoir fracability and can be used as characteristic parameters. The established model can effectively distinguish the difference of reservoir fracability along the wellbore axis, and select the fractures in the fracturable well section of the same type of reservoir, which is expected to improve the effect of balanced fracturing.