{"title":"Deep Bayesian Neural Networks for Fault Identification and Uncertainty Quantification","authors":"L. Mosser, S. Purves, E. Naeini","doi":"10.3997/2214-4609.202011775","DOIUrl":"https://doi.org/10.3997/2214-4609.202011775","url":null,"abstract":"Summary The interpretation of faults within a geological basin or reservoir from seismic data is a time-consuming, and often manual task associated with high uncertainties. Recently, numerous approaches using machine learning, especially various types of convolutional neural networks, have been presented to automate the process of identifying fault planes within seismic images, which have been shown to outperform traditional fault detection techniques. While these proposed methods show good performance, many of these approaches do not allow investigation of the associated uncertainties that arise in the fault identification process. In this study, we present an application of Bayesian deep convolutional neural networks for identifying faults within seismic datasets. Using an approximate Bayesian inference method a Bayesian deep neural network was trained on a large dataset of synthetic faulted seismic images. The model is then applied to a benchmark dataset and a real data case from NW shelf Australia to identify fault planes, and to investigate the associated uncertainty in the predictive distribution.","PeriodicalId":356678,"journal":{"name":"First EAGE Digitalization Conference and Exhibition","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126150841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
I. Varfolomeev, N. Evseev, O. Ridzel, V. Abashkin, A. Zozulya, S. Karpukhin, M. Miletsky
{"title":"Digital Multiscale Flow Modeling for Fractured Carbonates with Hessian-Based Cracks Detection","authors":"I. Varfolomeev, N. Evseev, O. Ridzel, V. Abashkin, A. Zozulya, S. Karpukhin, M. Miletsky","doi":"10.3997/2214-4609.202032039","DOIUrl":"https://doi.org/10.3997/2214-4609.202032039","url":null,"abstract":"","PeriodicalId":356678,"journal":{"name":"First EAGE Digitalization Conference and Exhibition","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124803874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Digitalization for Data Liberation","authors":"Z. Manan, A. Hazet, T. Bramono, D. Galih","doi":"10.3997/2214-4609.202032067","DOIUrl":"https://doi.org/10.3997/2214-4609.202032067","url":null,"abstract":"","PeriodicalId":356678,"journal":{"name":"First EAGE Digitalization Conference and Exhibition","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115490838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Productivity Prediction Integrating Data-Driven Method, Deep Neural Network and Exploratory Data Analysis in Montney Shale Plays","authors":"D. Han, S. Kwon, H. Son","doi":"10.3997/2214-4609.202032077","DOIUrl":"https://doi.org/10.3997/2214-4609.202032077","url":null,"abstract":"","PeriodicalId":356678,"journal":{"name":"First EAGE Digitalization Conference and Exhibition","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115509742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Standardized Direct Data Transfers Between Applications Accelerates Workflows and Improves Operational Adoption of Innovative Technologie","authors":"P. Neri, R. Philo, D. Wallis","doi":"10.3997/2214-4609.202032043","DOIUrl":"https://doi.org/10.3997/2214-4609.202032043","url":null,"abstract":"","PeriodicalId":356678,"journal":{"name":"First EAGE Digitalization Conference and Exhibition","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123719137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Uncover 2% Advanced Production Optimization across Complex Operational Plants through Industry 4.0, AI and Digital Twin","authors":"D. Piotrowski, J. Kalagnnanam","doi":"10.3997/2214-4609.202032027","DOIUrl":"https://doi.org/10.3997/2214-4609.202032027","url":null,"abstract":"","PeriodicalId":356678,"journal":{"name":"First EAGE Digitalization Conference and Exhibition","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122005995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Study on Geological Feature Extraction from FMI Logging Data by Using Deep Learning Neural Network","authors":"H. Wang, J. Gao, H. Chen","doi":"10.3997/2214-4609.202032051","DOIUrl":"https://doi.org/10.3997/2214-4609.202032051","url":null,"abstract":"","PeriodicalId":356678,"journal":{"name":"First EAGE Digitalization Conference and Exhibition","volume":"139 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122458816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}