Multi-task learning for digital rock segmentation and characteristic parameters computation

Abstract

“Image-and-compute” is the key paradigm of digital rock physics (DRP), the two main processes are relatively independent. Deep learning algorithm has been well applied in the field of digital rock image processing and numerical computation, but the models of different tasks are lack of correlation, and there is still a large space to improve the accuracy and efficiency. Multi-task learning algorithm can integrate image processing and numerical computation technology into the same model and share effective information of different tasks, which has great significance to simplify the workflow of DRP and improve the computation accuracy of the model. A distributed multi-task learning neural network (DMTNN) is designed by simulating the working sequence of DRP, which can accomplish digital rock image segmentation, petrophysical and elastic parameters computation simultaneously. DMTNN adopts the strategy of series connection and parallel connection between tasks, the segmentation and petrophysical parameters computation tasks provide intuitive low-level features as auxiliary information to improve the accuracy of abstract high-level targets. Moreover, a dynamic weight strategy is applied to solve the problem of unbalanced convergence caused by mutual interference between tasks in the training process. In order to verify the effectiveness of the proposed method, six different open source sandstones are used to form the training-set and testing-set. The results show that the average pixel accuracy (PA) of DMTNN's segmentation task is 0.97, the R2-score of porosity, shear modulus and bulk modulus can reach 0.92, 0.74 and 0.79 respectively. Furthermore, another completely untrained digital rock data is selected as the cross-dataset, in order to verify the robustness of the proposed model. The results show DMTNN in cross-dataset: the PA of segmentation task can reach more than 0.95, and the relative error of the elastic parameters computation tasks are lower than the traditional CNN.