PETROENG 7069 - Inverse problems and Uncertainty quantification

North Terrace Campus - Semester 2 - 2021

The course gives the theoretical basis and practical fundamentals for uncertainty quantification (both forward and backward propagation), inverse problem theory and numerical optimization, with an emphasis on their applications in subsurface flow problems. Global sensitivity analysis and forward propagation of uncertainty in the parameters of interest are discussed, and techniques used for drawing samples (unconditioned or conditioned) from multivariate distributions (with two-point and multi-point statistics) are reviewed. A particular attention is paid to inverse modeling in nonlinear problems with a Bayesian framework. Popular calibration algorithms, gradient-based (steepest descent and quasi-Newton) and derivative-free used for approximating/estimating Maximum a Posteriori and Maximum Likelihood are discussed. Gradient computation/approximation techniques (including adjoint method) in high-dimensional problems are also reviewed. The fundamentals of Markov chain Monte Carlo (MCMC) are discussed, and different techniques used for the approximation of posterior probability density function, including Metropolis?Hastings algorithm and data assimilation (ensemble Kalman filter and ensemble smoother), are presented and discussed. The application of surrogates/metamodels in uncertainty quantification is also studied. This course finally reviews several algorithms (including population-based methods) used to optimise single- and multi-objective problems, such as might be found field development and production optimisation.

  • General Course Information
    Course Details
    Course Code PETROENG 7069
    Course Inverse problems and Uncertainty quantification
    Coordinating Unit Australian School of Petroleum
    Term Semester 2
    Level Postgraduate Coursework
    Location/s North Terrace Campus
    Units 3
    Contact Block-format course with integrated lectures, practicals (computer based) and discussions.
    Available for Study Abroad and Exchange Y
    Assumed Knowledge MATHS 2107, MATHS 1011 AND PETROENG 3005 (OR EQUIVALENTS) and MATLAB PROGRAMMING
    Course Description The course gives the theoretical basis and practical fundamentals for uncertainty quantification (both forward and backward propagation), inverse problem theory and numerical optimization, with an emphasis on their applications in subsurface flow problems. Global sensitivity analysis and forward propagation of uncertainty in the parameters of interest are discussed, and techniques used for drawing samples (unconditioned or conditioned) from multivariate distributions (with two-point and multi-point statistics) are reviewed. A particular attention is paid to inverse modeling in nonlinear problems with a Bayesian framework. Popular calibration algorithms, gradient-based (steepest descent and quasi-Newton) and derivative-free used for approximating/estimating Maximum a Posteriori and Maximum Likelihood are discussed. Gradient computation/approximation techniques (including adjoint method) in high-dimensional problems are also reviewed. The fundamentals of Markov chain Monte Carlo (MCMC) are discussed, and different techniques used for the approximation of posterior probability density function, including Metropolis?Hastings algorithm and data assimilation (ensemble Kalman filter and ensemble smoother), are presented and discussed. The application of surrogates/metamodels in uncertainty quantification is also studied. This course finally reviews several algorithms (including population-based methods) used to optimise single- and multi-objective problems, such as might be found field development and production optimisation.
    Course Staff

    Course Coordinator: Dr Mohammad Sayyafzadeh

    Course Timetable

    The full timetable of all activities for this course can be accessed from Course Planner.

  • Learning Outcomes
    Course Learning Outcomes

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    University Graduate Attributes

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  • Learning & Teaching Activities
    Learning & Teaching Modes

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    Workload

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    Learning Activities Summary

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  • Assessment

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    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
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    Assessment Summary

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    Assessment Detail

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    Submission

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    Course Grading

    Grades for your performance in this course will be awarded in accordance with the following scheme:

    M10 (Coursework Mark Scheme)
    Grade Mark Description
    FNS   Fail No Submission
    F 1-49 Fail
    P 50-64 Pass
    C 65-74 Credit
    D 75-84 Distinction
    HD 85-100 High Distinction
    CN   Continuing
    NFE   No Formal Examination
    RP   Result Pending

    Further details of the grades/results can be obtained from Examinations.

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    Final results for this course will be made available through Access Adelaide.

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