Stochastic simulation of facies using deep convolutional generative adversarial network and image quilting. (December 2022)

• Record Type: Journal Article
• Authors: Liu, Xingye; Cheng, Jiwei; Cai, Yue; Mo, Qianwen; Li, Chao; Zu, Shaohuan
• Is Part Of: Marine and petroleum geology. Volume 146(2022)
• Publication Date: 2022-12
• Subjects: stochastic Simulation -- Facies -- Generative adversarial networks -- Reservoir characterization -- Deep learning; Submarine geology -- Periodicals; Petroleum -- Geology -- Periodicals; Géologie sous-marine -- Périodiques; Pétrole -- Géologie -- Périodiques; Petroleum -- Geology; Submarine geology; Periodicals; Electronic journals; 551.468
• DOI: 10.1016/j.marpetgeo.2022.105932
• Languages: English
• ISSNs: 0264-8172

Abstract

Abstract: Sedimentary facies simulation is one of the essential works in sedimentary environment analysis and reservoir characterization. The traditional facies simulation method is based on geostatistics. However, the traditional two-point geostatistics-based facies simulation method cannot characterize complex facies structures. Most multiple-point geostatistical simulation methods are unable to flexibly generate abundant geologic patterns. To address these shortcomings, we develop an intelligent method to automatically simulate sedimentary facies according to the training image provided by geologists. The method can learn efficient representations for complex facies architectures and obtain the simulation results in a larger area, rather than being limited to an area with the same size as the training image. First, we construct a deep convolutional generative adversarial network to extract the high-dimensional features of facies. Then, a large number of specific patterns are randomly generated based on these features. Thus, the diversity of geologic patterns is improved. Finally, the patterns are spliced together to obtain possible facies maps by using an improved image quilting algorithm. A model test is described and analyzed to demonstrate the effectiveness and reliability of the new method. The results are consistent with the actual situation in the aspect of variability and continuity. The method is also applied to non-stationary geological facies unconditional simulation. The successful application indicates that the method is able to learn the features of non-stationary geological phenomena, showing the practicability of the proposed methods. Highlights: Deep convolutional generative adversarial network is used to learn facies patterns. Facies map is synthetized by using the improved quilting algorithm. It is convenient for the simulation of non-stationary geological structure. The realization is consistent with the actual situation in the aspect of variability and continuity.