Uncertainties have a meaning: quantitative interpretation of the relationship between subsurface flow and geological data quality

Florian Wellmann

    Research output: ThesisDoctoral Thesis

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    Abstract

    [Truncated abstract] Long-term predictions of subsurface flow are important for societal issues such as groundwater flow, renewable and non-renewable energy resources, nuclear waste disposal and CO2 sequestration. In complex realistic settings, numerical simulations are dependent on the distribution of below-ground properties defined by geological models, constructed from observed data. The quality of the data therefore directly influences the predictions of subsurface flow. To date, no framework exists that allows a direct evaluation of effects of data quality on these flow fields. This thesis presents a first comprehensive method to analyze, visualize, quantify, and couple geological data uncertainty to flow field predictions. Methods are introduced to simulate and evaluate uncertainties in complex 3-D structural geological models. Based on probability distributions assigned to the underlying data, realizations of the geological model are created with an automated implicit modeling technique for complex 3-D spatial settings. The concept of information entropy is applied to visualize and analyze uncertainties in the resulting geological models. Information entropy values are a measure of the minimum number of geological units that can exist at any point in the domain. In addition, measures of mean model entropy can be used to derive quality estimates of the discretization required for geological modeling. Techniques are described that significantly simplify the integration of complex geological modeling into flow simulation, allowing an automatic update of flow and temperature outputs when data in the geological model are added or changed.
    Original languageEnglish
    QualificationDoctor of Philosophy
    Publication statusUnpublished - 2011

    Fingerprint

    subsurface flow
    data quality
    entropy
    flow field
    prediction
    modeling
    waste disposal
    carbon sequestration
    radioactive waste
    groundwater flow
    simulation
    temperature

    Cite this

    @phdthesis{297de09821bf4c368cbaa4d178a7f8d0,
    title = "Uncertainties have a meaning: quantitative interpretation of the relationship between subsurface flow and geological data quality",
    abstract = "[Truncated abstract] Long-term predictions of subsurface flow are important for societal issues such as groundwater flow, renewable and non-renewable energy resources, nuclear waste disposal and CO2 sequestration. In complex realistic settings, numerical simulations are dependent on the distribution of below-ground properties defined by geological models, constructed from observed data. The quality of the data therefore directly influences the predictions of subsurface flow. To date, no framework exists that allows a direct evaluation of effects of data quality on these flow fields. This thesis presents a first comprehensive method to analyze, visualize, quantify, and couple geological data uncertainty to flow field predictions. Methods are introduced to simulate and evaluate uncertainties in complex 3-D structural geological models. Based on probability distributions assigned to the underlying data, realizations of the geological model are created with an automated implicit modeling technique for complex 3-D spatial settings. The concept of information entropy is applied to visualize and analyze uncertainties in the resulting geological models. Information entropy values are a measure of the minimum number of geological units that can exist at any point in the domain. In addition, measures of mean model entropy can be used to derive quality estimates of the discretization required for geological modeling. Techniques are described that significantly simplify the integration of complex geological modeling into flow simulation, allowing an automatic update of flow and temperature outputs when data in the geological model are added or changed.",
    keywords = "Uncertainty, Geological modeling, Flow simulation, Geothermics, Information entropy, Entropy production",
    author = "Florian Wellmann",
    year = "2011",
    language = "English",

    }

    TY - THES

    T1 - Uncertainties have a meaning: quantitative interpretation of the relationship between subsurface flow and geological data quality

    AU - Wellmann, Florian

    PY - 2011

    Y1 - 2011

    N2 - [Truncated abstract] Long-term predictions of subsurface flow are important for societal issues such as groundwater flow, renewable and non-renewable energy resources, nuclear waste disposal and CO2 sequestration. In complex realistic settings, numerical simulations are dependent on the distribution of below-ground properties defined by geological models, constructed from observed data. The quality of the data therefore directly influences the predictions of subsurface flow. To date, no framework exists that allows a direct evaluation of effects of data quality on these flow fields. This thesis presents a first comprehensive method to analyze, visualize, quantify, and couple geological data uncertainty to flow field predictions. Methods are introduced to simulate and evaluate uncertainties in complex 3-D structural geological models. Based on probability distributions assigned to the underlying data, realizations of the geological model are created with an automated implicit modeling technique for complex 3-D spatial settings. The concept of information entropy is applied to visualize and analyze uncertainties in the resulting geological models. Information entropy values are a measure of the minimum number of geological units that can exist at any point in the domain. In addition, measures of mean model entropy can be used to derive quality estimates of the discretization required for geological modeling. Techniques are described that significantly simplify the integration of complex geological modeling into flow simulation, allowing an automatic update of flow and temperature outputs when data in the geological model are added or changed.

    AB - [Truncated abstract] Long-term predictions of subsurface flow are important for societal issues such as groundwater flow, renewable and non-renewable energy resources, nuclear waste disposal and CO2 sequestration. In complex realistic settings, numerical simulations are dependent on the distribution of below-ground properties defined by geological models, constructed from observed data. The quality of the data therefore directly influences the predictions of subsurface flow. To date, no framework exists that allows a direct evaluation of effects of data quality on these flow fields. This thesis presents a first comprehensive method to analyze, visualize, quantify, and couple geological data uncertainty to flow field predictions. Methods are introduced to simulate and evaluate uncertainties in complex 3-D structural geological models. Based on probability distributions assigned to the underlying data, realizations of the geological model are created with an automated implicit modeling technique for complex 3-D spatial settings. The concept of information entropy is applied to visualize and analyze uncertainties in the resulting geological models. Information entropy values are a measure of the minimum number of geological units that can exist at any point in the domain. In addition, measures of mean model entropy can be used to derive quality estimates of the discretization required for geological modeling. Techniques are described that significantly simplify the integration of complex geological modeling into flow simulation, allowing an automatic update of flow and temperature outputs when data in the geological model are added or changed.

    KW - Uncertainty

    KW - Geological modeling

    KW - Flow simulation

    KW - Geothermics

    KW - Information entropy

    KW - Entropy production

    M3 - Doctoral Thesis

    ER -