• Login
    View Item 
    •   Home
    • Research
    • Conference Papers
    • View Item
    •   Home
    • Research
    • Conference Papers
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of KAUSTCommunitiesIssue DateSubmit DateThis CollectionIssue DateSubmit Date

    My Account

    Login

    Quick Links

    Open Access PolicyORCID LibguideTheses and Dissertations LibguideSubmit an Item

    Statistics

    Display statistics

    Bayesian Long-Short Term Memory for History Matching in Reservoir Simulations

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Type
    Conference Paper
    Authors
    Santoso, Ryan cc
    He, Xupeng cc
    Alsinan, Marwa
    Kwak, Hyung
    Hoteit, Hussein cc
    KAUST Department
    Earth Science and Engineering Program
    Energy Resources & Petroleum Engineering
    Physical Science and Engineering (PSE) Division
    Energy Resources and Petroleum Engineering Program
    Ali I. Al-Naimi Petroleum Engineering Research Center (ANPERC)
    Date
    2021-10-19
    Online Publication Date
    2021-10-19
    Print Publication Date
    2021-10-19
    Permanent link to this record
    http://hdl.handle.net/10754/672970
    
    Metadata
    Show full item record
    Abstract
    History matching is critical in subsurface flow modeling. It is to align the reservoir model with the measured data. However, it remains challenging since the solution is not unique and the implementation is expensive. The traditional approach relies on trial and error, which are exhaustive and labor-intensive. In this study, we propose a new workflow utilizing Bayesian Markov Chain Monte Carlo (MCMC) to automatically and accurately perform history matching. We deliver four novelties within the workflow: 1) the use of multi-resolution low-fidelity models to guarantee high-quality matching, 2) updating the ranges of priors to assure convergence, 3) the use of Long-Short Term Memory (LSTM) network as a low-fidelity model to produce continuous time-response, and 4) the use of Bayesian optimization to obtain the optimum low-fidelity model for Bayesian MCMC . We utilize the first SPE comparative model as the physical and high-fidelity model. It is a gas injection into an oil reservoir case, which is the gravity-dominated process. The coarse low-fidelity model manages to provide updated priors that increase the precision of Bayesian MCMC. The Bayesian-optimized LSTM has successfully captured the physics in the high-fidelity model. The Bayesian-LSTM MCMC produces an accurate prediction with narrow uncertainties. The posterior prediction through the high-fidelity model ensures the robustness and precision of the workflow. This approach provides an efficient and high-quality history matching for subsurface flow modeling.
    Citation
    Santoso, R., He, X., Alsinan, M., Kwak, H., & Hoteit, H. (2021). Bayesian Long-Short Term Memory for History Matching in Reservoir Simulations. Day 1 Tue, October 26, 2021. doi:10.2118/203976-ms
    Sponsors
    We would like to thank CMG Ltd. for providing the IMEX academic license, and UQLab for the software license. We would also like to thank KAUST and Saudi Aramco for the support of this work.
    Publisher
    SPE
    DOI
    10.2118/203976-ms
    Additional Links
    https://onepetro.org/spersc/proceedings/21RSC/1-21RSC/D011S014R005/470806
    ae974a485f413a2113503eed53cd6c53
    10.2118/203976-ms
    Scopus Count
    Collections
    Conference Papers; Energy Resources and Petroleum Engineering Program; Ali I. Al-Naimi Petroleum Engineering Research Center (ANPERC); Physical Science and Engineering (PSE) Division; Earth Science and Engineering Program

    entitlement

     
    DSpace software copyright © 2002-2023  DuraSpace
    Quick Guide | Contact Us | KAUST University Library
    Open Repository is a service hosted by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items. For anonymous users the allowed maximum amount is 50 search results.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.