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    Ensemble Based Reservoir Modeling

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    Wan Norain Awang Long
    This document summarizes an ensemble-based reservoir modeling approach used by Eni to model a complex turbidite reservoir with uncertainties in faulting and channeling. The solution involved implementing an interdisciplinary modeling workflow to create an ensemble of 100 equally probable matched reservoir models using ResX. This allowed for geologically consistent explanations of static and dynamic data while quantifying uncertainty in forecasts and model parameters. The integrated workflow ensured geological consistency and repeatability in reservoir modeling and data assimilation.

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    Ensemble Based Reservoir Modeling

    Uploaded by

    Wan Norain Awang Long
    79 views2 pages
    This document summarizes an ensemble-based reservoir modeling approach used by Eni to model a complex turbidite reservoir with uncertainties in faulting and channeling. The solution involved implementing an interdisciplinary modeling workflow to create an ensemble of 100 equally probable matched reservoir models using ResX. This allowed for geologically consistent explanations of static and dynamic data while quantifying uncertainty in forecasts and model parameters. The integrated workflow ensured geological consistency and repeatability in reservoir modeling and data assimilation.

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    This document summarizes an ensemble-based reservoir modeling approach used by Eni to model a complex turbidite reservoir with uncertainties in faulting and channeling. The solution involved implementing an interdisciplinary modeling workflow to create an ensemble of 100 equally probable matched reservoir models using ResX. This allowed for geologically consistent explanations of static and dynamic data while quantifying uncertainty in forecasts and model parameters. The integrated workflow ensured geological consistency and repeatability in reservoir modeling and data assimilation.

    Copyright:

    © All Rights Reserved
    Download as PDF, TXT or read online from Scribd
    Download as pdf or txt
    79 views2 pages

    Ensemble Based Reservoir Modeling

    Uploaded by

    Wan Norain Awang Long
    This document summarizes an ensemble-based reservoir modeling approach used by Eni to model a complex turbidite reservoir with uncertainties in faulting and channeling. The solution involved implementing an interdisciplinary modeling workflow to create an ensemble of 100 equally probable matched reservoir models using ResX. This allowed for geologically consistent explanations of static and dynamic data while quantifying uncertainty in forecasts and model parameters. The integrated workflow ensured geological consistency and repeatability in reservoir modeling and data assimilation.

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    Ensemble Based Reservoir Modeling

    Complex channelized two million cell reservoir model, Courtesy of Eni

    Creating consistent geological models with a solution


    that integrates geological and engineering understanding

    Objective History Matching Challenges techniques such as design of


    Model and history match (HM) a Typical challenges in traditional experiments or adjoint-based
    complex turbidite reservoir with reservoir modeling and history approaches. These methods explore
    large uncertainties in faulting and matching include: larger solution spaces, but they are
    channeling still constrained by practical or
    • Intensive, often manual, iterations algorithmic constraints which limit the
    Solution • Consistent integration of G&G and number of uncertainty parameters.
    Implementation of an engineering understanding
    interdisciplinary modeling workflow • Capturing and propagating Ensemble Based Modeling
    to create an ensemble of matched uncertainty The Ensemble Kalman based method
    models using ResX • Multiple, equally likely solutions makes it possible to generate multiple
    Outcomes • Updating model(s) with new data matched models using any size of
    Reliable reservoir models that offer parameterization. It is an efficient
    The way in which these challenges
    geologically consistent explanations solution when it is tied in with an
    are approached can directly affect
    of static and dynamic data where appropriate reservoir modeling
    the commercial outcome of projects.
    uncertainty is quantified in the approach, suitable parameterization,
    As a result, there are a wide variety
    forecast and model parameters and repeatable workflows. For Eni’s
    of approaches in methodology and
    complex reservoir, ResX’s integrated
    asset team workflows.
    methodology made it possible to
    The trial-and-error HM method retain the necessary geological
    continues to be prevalent in most consistency.
    projects, but alongside this there is
    increasing use of more automated
    Conceptual representation of the ensemble based integrated workflow.

    Solution Highlights
    • 100 equally probable reservoir models were created that consistently honor the current measurements of both
    static and dynamic data, while capturing and propagating the uncertainty in the modeling and data assimilation
    process.

    • ResX was integrated into five workflows covering the entire reservoir modeling process including:
    1) reservoir modeling; 2) ensemble generation; 3) data assimilation; 4) validation; 5) forecast (see figure above).

    • The reservoir modeling workflow consists of three main processes: structural modeling and gridding,
    facies modeling, and petrophysical modeling.

    • The chosen parameterization includes both scalar variables and 3D grid properties, for a total of almost
    10 million parameters.

    • The approach ensured geological consistency and workflow repeatability in the reservoir modeling and data
    assimilation process.

    • The repeatability makes it possible to easily integrate new static and dynamic data and to re-parametrize the
    model when the match quality is unsatisfactory.

    • Through the analysis of the updated model properties, new reservoir insights were identified including flow
    barriers in certain areas and in-place volume constraints.

    For more details check out SPE 186049 Resoptima.com

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