Training image optimization method based on convolutional neural network and its application in discrete fracture network model selection

• Published in: Lithosphere Vol. 2021; no. Special 1
• Main Authors: Yu Siyu, Yu Siyu, Li Shaohua, Li Shaohua
• Format: Journal Article
• Language: English
• Published: GeoScienceWorld 01.03.2021
• Subjects: applications; Economic geology; energy sources; fractures; image analysis; multipoint statistics; neural networks; optimization; petroleum engineering; reservoir properties; sampling; sensitivity analysis; statistical analysis; techniques
• ISSN: 1941-8264; 1947-4253
• DOI: 10.2113/2021/4963324

Training image (TI) is important for multipoint statistics simulation method (MPS), since it captures the spatial geological pattern of target reservoir to be modeled. Generally, one optimal TI is selected before applying MPS by evaluating the similarities between many TIs and the well interpretations of target reservoir. In this paper, we propose a new training image optimization approach based on the convolutional neural network (CNN). First, candidate TIs were randomly sampled several times to obtain the sample dataset. Then, the CNN was used to conduct transfer learning for all samples, and finally, the optimal TI of the conditioning well data is selected through the trained CNN model. By taking advantage of the strong learning ability of CNN in image feature recognition, the proposed method can automatically identify differences in spatial features between the conditioning well data and the samples of the training image. Hence, it effectively resolves the difficulty of spatial matching between discrete datapoints and grid structures. We demonstrated the applicability of our model via 2D and 3D training image selection examples. The proposed methods effectively selected the appropriate TI, and then the pretreatment techniques for improving the accuracy of continuous TI selection were achieved. Moreover, the proposed method was successfully applied to training image selection of a discrete fracture network model. Finally, sensitivity analysis was carried out to show that sufficient conditioning data volume can reduce the uncertainty of the optimization results. By comparing with the improved MDevD method, the advantages of the new method are verified in terms of efficiency and reliability.