Multi-cleat coal seam borehole wall collapse pressure prediction method based on machine learning

The invention relates to a multi-cleat coal seam borehole wall collapse pressure prediction method based on machine learning, and belongs to the field of oil and gas drilling engineering, and the method comprises the steps: calculating borehole wall stability key rock mechanics parameters through a rock mechanics calculation model and a ground stress calculation model of a target coal seam; determining cleat development characteristics of the target coal seam according to core observation and imaging logging data; calculating well circumference stress according to the obtained ground stress, well track parameters and formation pore pressure, and calculating collapse pressure on the basis of a Mogi-Coulomb intermediate principal stress rule in combination with specific cleat development characteristics; seven parameters are selected as input parameters, and a machine learning method is selected to carry out collapse pressure calculation; a fitting model between the collapse pressure and the key influence facto