Physical embedded deep learning formation pressure prediction method, device, medium and equipment

A physical embedded deep learning formation pressure prediction method, characterized in that, comprising the following steps:

• S1. Collection of data during oil and gas exploration and development;
• S2. According to the data collected in step S1, the logging impedance quality factor Q is calculated by the slippage windows method;
• S3. According to the data collected in step St and the logging impedance quality factor Q obtained in step S2, a Q-pressure rock physical model based on well-logging data is established and cross-verified;
• S4. According to the data collected in step S1, the well seismic correlation analysis is performed on the drilling position, and the best frequency component of the seismic data is determined. Based on the optimal frequency component, the Q-pressure rock physical model based on well-logging data obtained in step S3 is extended to the well-side seismic data, and the Q-pressure rock physical model based on seismic data is established;
• S5. The nonlinear activation function of Caianiello convolution neuron is replaced with the Q-pressure rock physics model based on seismic data obtained in Step S4, mid the deep learning convolution neural networks are constructed by using the convolution neuron to obtain the physical embedded deep learning formation pressure prediction networks CCNNs;
• S6. The physical embedded deep learning formation pressure prediction networks CCNNs obtained in Step S5 is used to invert formation pressure.