{"title":"Information Theory Considerations In Patch-Based Training Of Deep Neural Networks On Seismic Time-Series","authors":"J. Dramsch, M. Lüthje","doi":"10.3997/2214-4609.201803020","DOIUrl":"https://doi.org/10.3997/2214-4609.201803020","url":null,"abstract":"Summary Recent advances in machine learning relies on convolutional deep neural networks. These are often trained on cropped image patches. Pertaining to non-stationary seismic signals this may introduce low frequency noise and non-generalizability.","PeriodicalId":231338,"journal":{"name":"First EAGE/PESGB Workshop Machine Learning","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124762018","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":"How Machine Learning Is Replacing Conventional Interpretation","authors":"D. Sacrey, R. Roden","doi":"10.3997/2214-4609.201803011","DOIUrl":"https://doi.org/10.3997/2214-4609.201803011","url":null,"abstract":"This presentation shows severaed classification process in successful case histories of the sample-basully finding hydrocarbons and delineating reservoir limits. This type of machine learning is especially good for thin bed exploration as it allows for stratigraphic pattern recognition below conventional seismic tuning.","PeriodicalId":231338,"journal":{"name":"First EAGE/PESGB Workshop Machine Learning","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124539473","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":"Deep Learning History Matching For Real Time Production Forecasting","authors":"Kelvin Loh, P. S. Omrani, R. V. D. Linden","doi":"10.3997/2214-4609.201803016","DOIUrl":"https://doi.org/10.3997/2214-4609.201803016","url":null,"abstract":"The forecasting of gas production from mature gas wells, due to their complex end-of-life behaviour, is challenging and often associated with uncertainties (both measurements and modelling uncertainties). Yet, having good forecasts are crucial for operational decision making. In this paper, we present a purely black-box based approach, which combines the use of a data assimilation method, the Ensemble Kalman Filter (EnKF) and a modified deep LSTM model as the prediction model within the approach. This approach is tested on two mature gas wells in the North Sea which were suffering from salt precipitation. Results showed that the approach of combining a deep LSTM model within EnKF can be effective when deployed in a real-time production optimization environment. We observed that having the EnKF increases the robustness of the forecasts by the black box prediction model while reducing computational cost of retraining the black-box models for every individual well.","PeriodicalId":231338,"journal":{"name":"First EAGE/PESGB Workshop Machine Learning","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127367573","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":"An Extension For The RA Methodology: Stability Analysis","authors":"E. V. Brazil, Reinaldo Silva, L. Farias","doi":"10.3997/2214-4609.201803029","DOIUrl":"https://doi.org/10.3997/2214-4609.201803029","url":null,"abstract":"We present an extension for a methodology proposed by Perez-Valiente et al (2014), known as Reservoirs Analogues (RA). This method finds analogues using machine learning to complete a dataset. Our concern is this methodology does not track error carried from the imputation of missing values until ranking lists of analogues. This study aims to analyze the inherent uncertainty of this step discussing how it can be beneficial to obtain accurate information for reservoirs with limited information.","PeriodicalId":231338,"journal":{"name":"First EAGE/PESGB Workshop Machine Learning","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127307522","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":"Data-Driven Signal Recognition- A Machine Learning Application For The Real-Time Microseismic Monitoring","authors":"A. Shamsa, M. Paydayesh","doi":"10.3997/2214-4609.201803007","DOIUrl":"https://doi.org/10.3997/2214-4609.201803007","url":null,"abstract":"A simple and robust machine learning technique is applied to automate signal detection and analyse recorded microseismic data. The method’s performance is tested and evaluated on real data. The fracture signals were well-detected using the proposed workflow and techniques when more data were introduced. In contrast to conventional methods, the techniques implemented herein described work on training the model prediction with additional data without restarting from the beginning, making them viable for continuous online learning. This method attempts to remove the burden of labour-intensive processing of microseismic data and replace it with a faster, cheaper, and more accurate way of achieving signal detection.","PeriodicalId":231338,"journal":{"name":"First EAGE/PESGB Workshop Machine Learning","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130323416","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}
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