{"title":"基于地质信息拟井和迁移学习的稀疏井区地震岩相深度学习预测","authors":"Jinyu Meng , Luanxiao Zhao , Minghui Xu , Hua Chen","doi":"10.1016/j.jappgeo.2025.105891","DOIUrl":null,"url":null,"abstract":"<div><div>Accurate lithology prediction from seismic data plays a critical role in unraveling the complexities of subsurface geology, enabling informed decision-making in geo-energy exploration and production, geological storage of CO2, and geological hazard assessment. Deep learning approaches, with their capabilities of feature extraction, mapping non-linear relationships, and handling high dimensional features, show great potential in seismic reservoir characterization. However, limited well logging data due to high drilling costs pose challenges for deep learning model training, particularly in frontier exploration and early development stage. Existing data augmentation methods often focus on increasing data quantity without effectively utilizing geological knowledge, potentially limiting their ability to capture the realistic complexity of data. To address this challenge, especially in sparse well regions, we propose a geostatistics-based pseudo-well construction methodology. By considering geologic stratification, the lithofacies are simulated using the Markov chain method, and the corresponding elastic features are simulated using sequential Gaussian simulation. This methodology enhances the reliability and accuracy of pseudo-well construction, with more geological consistency with the actual wells. Then, using the limited actual well data, we use transfer learning strategy to predict lithofacies from prestack data and seismic inversion via supervised convolutional neural network. We employ the proposed methodology in a coal-bearing clastic reservoir. Based on the blind well test, the strategy of combining pseudo-well data and transfer learning leads to a notable enhancement in the F1 score of sandstone from 57.45 % to 62.16 %, as well as an overall F1 score improvement from 52.92 % to 57.89 %. We apply this method to 2D seismic profiles (prestack data and inversion results), and the predicted spatial distribution of the lithofacies shows better agreement with the lithofacies in actual wells and more geological reasonableness.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"242 ","pages":"Article 105891"},"PeriodicalIF":2.1000,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Deep learning-based seismic lithofacies prediction in sparse well areas via geology-informed pseudo-well construction and transfer learning\",\"authors\":\"Jinyu Meng , Luanxiao Zhao , Minghui Xu , Hua Chen\",\"doi\":\"10.1016/j.jappgeo.2025.105891\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Accurate lithology prediction from seismic data plays a critical role in unraveling the complexities of subsurface geology, enabling informed decision-making in geo-energy exploration and production, geological storage of CO2, and geological hazard assessment. Deep learning approaches, with their capabilities of feature extraction, mapping non-linear relationships, and handling high dimensional features, show great potential in seismic reservoir characterization. However, limited well logging data due to high drilling costs pose challenges for deep learning model training, particularly in frontier exploration and early development stage. Existing data augmentation methods often focus on increasing data quantity without effectively utilizing geological knowledge, potentially limiting their ability to capture the realistic complexity of data. To address this challenge, especially in sparse well regions, we propose a geostatistics-based pseudo-well construction methodology. By considering geologic stratification, the lithofacies are simulated using the Markov chain method, and the corresponding elastic features are simulated using sequential Gaussian simulation. This methodology enhances the reliability and accuracy of pseudo-well construction, with more geological consistency with the actual wells. Then, using the limited actual well data, we use transfer learning strategy to predict lithofacies from prestack data and seismic inversion via supervised convolutional neural network. We employ the proposed methodology in a coal-bearing clastic reservoir. Based on the blind well test, the strategy of combining pseudo-well data and transfer learning leads to a notable enhancement in the F1 score of sandstone from 57.45 % to 62.16 %, as well as an overall F1 score improvement from 52.92 % to 57.89 %. We apply this method to 2D seismic profiles (prestack data and inversion results), and the predicted spatial distribution of the lithofacies shows better agreement with the lithofacies in actual wells and more geological reasonableness.</div></div>\",\"PeriodicalId\":54882,\"journal\":{\"name\":\"Journal of Applied Geophysics\",\"volume\":\"242 \",\"pages\":\"Article 105891\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2025-07-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Applied Geophysics\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0926985125002721\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"GEOSCIENCES, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Applied Geophysics","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0926985125002721","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
Deep learning-based seismic lithofacies prediction in sparse well areas via geology-informed pseudo-well construction and transfer learning
Accurate lithology prediction from seismic data plays a critical role in unraveling the complexities of subsurface geology, enabling informed decision-making in geo-energy exploration and production, geological storage of CO2, and geological hazard assessment. Deep learning approaches, with their capabilities of feature extraction, mapping non-linear relationships, and handling high dimensional features, show great potential in seismic reservoir characterization. However, limited well logging data due to high drilling costs pose challenges for deep learning model training, particularly in frontier exploration and early development stage. Existing data augmentation methods often focus on increasing data quantity without effectively utilizing geological knowledge, potentially limiting their ability to capture the realistic complexity of data. To address this challenge, especially in sparse well regions, we propose a geostatistics-based pseudo-well construction methodology. By considering geologic stratification, the lithofacies are simulated using the Markov chain method, and the corresponding elastic features are simulated using sequential Gaussian simulation. This methodology enhances the reliability and accuracy of pseudo-well construction, with more geological consistency with the actual wells. Then, using the limited actual well data, we use transfer learning strategy to predict lithofacies from prestack data and seismic inversion via supervised convolutional neural network. We employ the proposed methodology in a coal-bearing clastic reservoir. Based on the blind well test, the strategy of combining pseudo-well data and transfer learning leads to a notable enhancement in the F1 score of sandstone from 57.45 % to 62.16 %, as well as an overall F1 score improvement from 52.92 % to 57.89 %. We apply this method to 2D seismic profiles (prestack data and inversion results), and the predicted spatial distribution of the lithofacies shows better agreement with the lithofacies in actual wells and more geological reasonableness.
期刊介绍:
The Journal of Applied Geophysics with its key objective of responding to pertinent and timely needs, places particular emphasis on methodological developments and innovative applications of geophysical techniques for addressing environmental, engineering, and hydrological problems. Related topical research in exploration geophysics and in soil and rock physics is also covered by the Journal of Applied Geophysics.