This invention relates to a method for predicting
fracture pressure in layered shale wells with extended reach based on a
machine learning model. S1: Acquire drilled well data and collect downhole core samples, conduct anisotropic tensile mechanical experiments on shale along the
bedding direction, and construct shale fracture criteria based on the experimental results; S2: Construct a
fracture pressure model for layered shale wells with extended reach, and correct and integrate the calculated
fracture pressure values with field
fracture test data; S3: Calculate the fracture pressure using a mechanical model based on the
wellbore structure conditions of highly deviated wells, verify it using field low-
pressure data, and form a fracture pressure calculation and analysis
database; S4:
Train the prediction model based on
machine learning methods, determine the WT-LSTM model structure, set hidden
layers, and
train it; S5: Optimize the
machine learning-based fracture pressure model through
hyperparameter adjustment,
feature selection, and importance assessment. This invention comprehensively improves the accuracy of fracture pressure prediction in highly deviated wells by integrating
multiple factors, provides data support for the design of fracturing
engineering in complex layered shale, helps to rationally select parameters for extended reach well construction, improves adaptability and safety, and provides data support for the development of layered
shale oil and gas resources.