Feng Zhang

The vertical heterogeneity of continental shale reservoirs is strong, the difference between lithology and stress between layers is large, the weak interface between layers develops, and the hydraulic fracture penetration and expansion are difficult, resulting in poor fracturing transformation effect. In view of this, based on the finite element and cohesive element method, this paper established a fluid-solid coupling model for the hydraulic fracture propagation through the continental shale and studied the control mechanism and influence law of various geological and engineering parameters on the hydraulic fracture propagation through the continental shale reservoir using single factor and orthogonal test analysis methods. Interfacial cementation strength between high layers, high vertical stress difference, low interlaminar stress difference, low tensile strength difference, low elastic modulus difference, high pressure fracturing fluid viscosity, and high injection displacement are conducive to the penetration and expansion of hydraulic fractures. The primary and secondary order of influence degree of each factor is: interlaminar interface cementation strength > interlaminar stress difference/tensile strength difference > fracturing fluid viscosity/injection displacement > vertical stress difference > elastic modulus. In addition, engineering application research has also been carried out, and it is recommended that the injection displacement during early construction should not be less than 3 m³/min, and the fracturing viscosity should not be less than 45 mPa·s. The field application effect is good, which verifies the engineering application value of the model.

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.