Construction of machine learning data set for geophysical logging inversion

Intelligent logging interpretation based on data-driven machine learning has promising prospects for significantly improving the efficiency of well logging data processing and interpretation. However, data-driven logging inversion, such as reservoir parameter prediction, faces challenges such as small sample size, limited labels, and poor interpretability. Typically, manually interpreted measured logging dataset is the main source of machine learning labels. Due to the complexity of subsurface fluid resources, the multiple solutions of logging inversion, and heterogeneity of formation, the reliability and quantity of labels constructed from measured data sets are questionable. This paper proposes a method for constructing machine learning datasets for logging inversion based on geological domain knowledge and petrophysical mechanism models by forward simulation. Starting from geological constraints, this method comprehensively considers the influences of borehole environment, logging instruments, formation models, and fluid distribution, logging data to generate logging dataset by forward simulation based on petrophysical domain knowledge. The model trained by generated dataset could achieve the fusion of mechanism model and data-driven approach. Numerical experiments show that the forward-synthesized well logging dataset effectively increases the sample and label quantity. By participating in the training of deep neural networks for the reservoir parameters prediction and reservoir fluid classification, it significantly improves the effectiveness of well logging reservoir parameter prediction models and promotes the development of data-driven and data-mechanism-driven methods of data and mechanism.