A reservoir parameters prediction method for geophysical logs based on transfer learning

Geophysical logging is the main measurement to evaluate reservoir parameters, such as porosity, permeability and saturation. Generally, the reservoir parameters prediction with logging data is based on petrophysical model and response functions, which requires clear relationship and mechanism. However, in the actual underground detection, there are strong uncertainty of the response mechanism of electrical, acoustic, nuclear radiation and nuclear magnetic resonance methods, which makes difficulty of the application of petrophysical mechanism model. With the maturity and extension of big data and machine learning, more researchers have paied attention to the application of artificial neural network in the reservoir parameters prediction with logs. It could fiind the mapping between geophysical logging data and reservoir parameters by constructing a suitable network model and training with high-quality labeled data, without the domain knowledge of geology, geophysics and petrophysics. In this paper, transfer learning is introduced to the reservoir parameters prediction with logs. Taken porosity prediction network model and porosity water saturation joint prediction neural network model as base models, permeability and water saturation prediction as target models, we use transfer learning to improve prediction performance and training efficiency. We also suggest a method of constructing transfer learning neural network model for reservoir paramenters prediction, and analysis the performance with logs from 64 wells. The preliminary results show that, in the best cases, the prediction performance of permeability model using transfer learning can be increased by 58.3%; the prediction performance of water saturation model using transfer learning can be increased by nearly 40%, and the calculation resources can be saved by 60%; the transfer parameters freezing training mode is more suitable for the porosity prediction model based transfer learning model, and it is more suitable to use the transfer parameters fine-tuning training mode for the porosity and water saturation joint prediction model based transfer learning model. In the future, it could be considered to use instance-based transfer learning solving the problem of simple samples; use feature-based transfer learning to reducing the impact of poor-quality labels on the model.