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    Class and Course
    1. NExT
    2. SLB Software
    3. MEPO Uncertainty and Optimization Fundamental

    MEPO Uncertainty and Optimization Fundamental

    • Foundation
    • 3 Days
    • classroom1Classroom
      • Average client rating (based on 263 attendee reviews)

    Sensitivity, subsurface uncertainty assessment, and history matching studies for calibrating reservoir simulation models are integral workflow tasks in many reservoir simulation studies. The course is designed to provide the petroleum engineers with software skill and modeling techniques required to perform the tasks of managing reservoir uncertainties and optimization.

    It discusses how to use MEPO uncertainty and optimization tools to perform sensitivity analysis, uncertainty assessment, and optimization (history matching) and also covers the proxy modeling concepts, benefits, limitations and its applications using the avaliable workflow tools in MEPO.

    The concepts behind the software technology and its practical appplications focusing on methodology, design and execution are also covered.

    The training exercises are workflow-driven intended to demonstrate the practical application scenarios and help participants understand the benefits of method applied.
     
    Day 1
    • Sensitivity and uncertainty assessment workflows using experimental
      design and sampling techniques
    1. Introduction to Sub-surface Uncertainty Quantification
    2. Basic definitions for uncertainty and risk
    3. Discussion on sources of uncertainty and risk in the petroleum industry
    4. Explanation of dependencies and interactions between input parameters
    5. Description on how to quantify and assess uncertainty using the Uncertainty and optimization tool in MEPO
    6. Expanation on the typical uncertainty analysis workflow supported in MEPO
    7.  Sensitivity analysis workflow and the steps involved in performing a sensitivity analysis
      •  design of the experiment
      •  how to assess the correlation of input variables and simulation results
      •  how to reduce the uncertain parameter space by considering important factors and reducing the problem complexity (uncertainty reduction)
    8. Discussion on the different terminologies used to express uncertainty and risk such as expected
      value, standard deviation, mean, cumulative distribution function, probability density function,
      distribution and ranges
    9. Explain what cumulative distribution function (CDF) is and how it is related to the probability
      density function (PDF)
    10. Explain the use of a CDF curve to present the probabilistic estimate of a reserves (P10, P50, and P90)
    11. How to set up uncertainty simulation runs in MEPO and results visualization for both sensitivity and uncertainty studies using the available visualization and results analysis tools
    • Exercises: Greenfield application scenarios.
    Day 2
    • Workflow customization and History matching workflow design including an introduction to optimization techniques.

      1. Discussion on MEPO processes which involves; Task management, Input parameter, Response parameters and Case management.
      2. Python and how to customize processing tasks using Python and also writing MEPO-specific Python scripts
      3. Pre-processing and post-processing loop in MEPO, including
        selected Python scripts
      4. How to use MEPO to aid the history matching process following the well-structured history matching workflow steps
        • Definition of objective function that measures the history matching quality
        • Define and evaluate an uncertainty matrix
        • Set up and use an optimization algorithm for the history matching process
        • Apply a selection of analysis tools to interpret results and
          monitor and measure performance
    • Exercises: Brownfield application scenarios.
    Day 3
    • Proxy modelling techniques and its applications.
    1. Explanation of Proxy modeling concepts, application, benefits and limitation
    2. Discussion on how to identify application scenarios for proxy models
    3. How to define a  training dataset for proxy model and creation of proxy model
    4. Validation of proxy model and how to create tests for evaluating proxy predictability
    • Exercises: Greenfield and brownfield application scenarios

    Development and exploration petroleum engineers, reservoir engineers, geoscientists, and any asset team members who want to get acquainted to Petrel reservoir engineering workflow.

    Module 1: Introduction to Sub-surface Uncertainty Quantification

    Module 2: Sensitivity study

    Module 3: Uncertainty assessment

    Module 4: Workflow customization

    Module 5: Assisted History Matching  

    Module 6: Proxy Modeling

    Ability to use Eclipse reservoir simulator

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