[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117881-en":3,"doc-seo-117881-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117881,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Embedding Reservoir Physics into Machine Learning - A Dissertation","This dissertation develops and evaluates proxy models for petroleum reservoir numerical simulation using scientific machine learning. Numerical reservoir simulation supports decisions across exploitation stages but is computationally expensive and may require thousands of runs. The work proposes Embed to Control and Observe using a convolutional autoencoder and a physical-loss control framework, including partial convolution to address non-active grid blocks. It also introduces physics-informed neural network methods with learned artificial viscosity for hyperbolic PDEs.","EMBEDDING RESERVOIR PHYSICS INTO MACHINE LEARNING  \nA Dissertation  \nby  \nEMILIO JOSE ROCHA COUTINHO  \nSubmitted to the Graduate and Professional School of Texas A&M University  \nin partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY  \nChair of Committee, Eduardo Gildin  \nCo-Chair of Committee, Thomas A. Blasingame Committee Members, Siddharth Misra  \nUlisses Braga-Neto  \nHead of Department, Jeff Spath  \nAugust 2022  \nMajor Subject: Petroleum Engineering  \nCopyright 2022 Emilio Jose Rocha Coutinho  \nABSTRACT  \nThe aim of this thesis is to explore and develop proxy models for a petroleum reservoir numerical simulator based on scientific machine learning methods. Numerical reservoir simulation is an essential tool used in all stages of petroleum reservoir exploitation. Several technical studies are performed using these simulations, supporting many business decisions. These simulations are computationally expensive, and the complexity of the analysis may require thousands of simulations. An active research area is searching for a proxy model which could estimate the simulation outputs with a fraction of its computational cost.  \nWe developed the Embed to Control and Observe method based on a convolutional autoencoderand the control system approach with physical loss functions. We showed our proxy model's potential by applying it to three reservoir models. The last two models contained non-active grid blocks, which most real reservoir simulation models also have. To overcome this, we introduce the use of partial convolutional layers in reservoir simulation applications. The error obtained on our application are considerably low, so a reliable proxy can be obtained by applying the proposed method.  \nPhysics Informed Neural Network has emerged as a powerful scientific machine learning tool. We developed a partial differential equation solver for hyperbolic problem that can automatically handle discontinuities. Our method can learn and localize the application of artificial viscosity during the neural network training procedure. We solved the Inviscid Burger's equation and the Buckley-Leverett problem. The method can potentially be applied to solve other hyperbolic PDE systems. We also formulate a two-dimensional two-phases reservoir simulation PDE set of equations to be used on the Physics Informed Neural Network framework.  \nThe significance of this study is that it shows the development and application of machine learning techniques associated with physical knowledge of the fluid flow phenomenon. These techniques have the potential to provide an inexpensive and reliable prediction of essential reservoir simulation outputs for the petroleum industry.  \nDEDICATION  \nTo my wife, Erica, who had shared with me all the good and not so good moments on this journey. Her support was essential to help me follow my dream to complete all the steps to success. To my daughter Letícia and my son João who had to face many physical and emotional absences  \nduring these four years of work.  \nTo my father, my mother, and my sister, which have always supported and encouraged me to overcome numerous difficulties to reach such ambitious goals.  \nACKNOWLEDGMENTS  \nPortions of this research were conducted using the advanced computing resources provided by Texas A&M High-Performance Research Computing. The Physics Informed Neural Network methods proposed in this research were implemented on a modified version of the TensorDiffEq package [1] . The author also acknowledges all the support provided by Petróleo Brasileiro S.A.  \nCONTRIBUTORS AND FUNDING SOURCES  \nContributors  \nThis work was supported by a dissertation committee consisting of Professors Eduardo Gildin [advisor], Professor Thomas A. Blasingame [co-advisor] and Professor Siddharth Misra of the Department of Petroleum Engineering and Professor Ulisses Braga-Neto of the Department of Electrical & Computer Engineering.  \nThe work presented on Chapter 3 had conceptual contributio","cbCaiaXic6PYeNHY","https://ap.wps.com/l/cbCaiaXic6PYeNHY","pdf",5148148,1,112,"English","en",105,"# Abstract\n# Contributions and Methods\n## Embed to Control and Observe\n## Partial Convolution for Non-Active Grid Blocks\n## Physics Informed Neural Networks for Hyperbolic PDEs\n# Validation and Applications\n# Significance","[{\"question\":\"What problem does the dissertation address in petroleum reservoir simulation?\",\"answer\":\"It targets the high computational cost of numerical reservoir simulation, which can require thousands of runs to support technical studies and business decisions.\"},{\"question\":\"What is the Embed to Control and Observe method based on?\",\"answer\":\"It is developed using a convolutional autoencoder combined with a control system approach that uses physical loss functions.\"},{\"question\":\"How does the work extend physics-informed neural networks for hyperbolic problems?\",\"answer\":\"It presents a PDE solver that can handle discontinuities and can learn and localize the application of artificial viscosity during neural network training.\"}]","Embedding Reservoir Physics into Machine Learning - 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