PETROENG 4064 - Data analytics in oil and gas industry

North Terrace Campus - Semester 2 - 2024

This course introduces the opportunities, challenges and current development of data analytics applications in oil and gas industry. The theory and fundamental equations, as well as understanding data driven methods are covered. Practical methods, real field examples will equip students to apply data analytics and machine learning methods in petroleum engineering. The course covers the following topics with specific applications in petroleum engineering: ? Overview of Data Analytics ? Introduction to Programming ? Python ? Univariate and Multivariate Descriptive Statistics ? Univariate and Multivariate Inferential Statistics and Predictive Analytics ? Machine Learning

  • General Course Information
    Course Details
    Course Code PETROENG 4064
    Course Data analytics in oil and gas industry
    Coordinating Unit Mining and Petroleum Engineering
    Term Semester 2
    Level Undergraduate
    Location/s North Terrace Campus
    Units 3
    Contact 48 hours - Intensive mode
    Available for Study Abroad and Exchange Y
    Assumed Knowledge Reservoir Engineering
    Course Description This course introduces the opportunities, challenges and current development of data analytics applications in oil and gas industry. The theory and fundamental equations, as well as understanding data driven methods are covered. Practical methods, real field examples will equip students to apply data analytics and machine learning methods in petroleum engineering.
    The course covers the following topics with specific applications in petroleum engineering:
    ? Overview of Data Analytics
    ? Introduction to Programming ? Python
    ? Univariate and Multivariate Descriptive Statistics
    ? Univariate and Multivariate Inferential Statistics and Predictive Analytics
    ? Machine Learning
    Course Staff

    Course Coordinator: Associate Professor Abbas Zeinijahromi

    Course Timetable

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

  • Learning Outcomes
    Course Learning Outcomes
    1 Learn how to use basic artificial neural networks
    2 Learn how to perform data clustering, feature extraction and classification
    3 Describe the fundamentals of Descriptive and Predictive Analytics
    4 Choose the most appropriate ML and DA model
    5 Use Python basic commands and deal with specialty data types
    6 Use Python most popular libraries for petroleum engineering data analytics
    7 Apply Python Machine Learning Packages in petroleum engineering applications

     
    The above course learning outcomes are aligned with the Engineers Australia Entry to Practice Competency Standard for the Professional Engineer. The course develops the following EA Elements of Competency to levels of introductory (A), intermediate (B), advanced (C):  
     
    1.11.21.31.41.51.62.12.22.32.43.13.23.33.43.53.6
    B C C C C C C C C B C C C C B C
    University Graduate Attributes

    This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:

    University Graduate Attribute Course Learning Outcome(s)

    Attribute 1: Deep discipline knowledge and intellectual breadth

    Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.

    1-7

    Attribute 2: Creative and critical thinking, and problem solving

    Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges.

    7

    Attribute 3: Teamwork and communication skills

    Graduates convey ideas and information effectively to a range of audiences for a variety of purposes and contribute in a positive and collaborative manner to achieving common goals.

    4,7

    Attribute 7: Digital capabilities

    Graduates are well prepared for living, learning and working in a digital society.

    1-7
  • Learning & Teaching Activities
    Learning & Teaching Modes

    0900 - 1030

    Lecture Presentation (AZ)

    Introduction

    Online only

    Lecture and exercises  (AZ)

    (AZ)

    Programming in Python

    Online only

    1030 - 1100

    Break

    Break

    1100 - 1230

    Lecture and exercises  (AZ)

    Programming in Python

    Online only

    Lecture and exercises  (AZ)

    Programming in Python

    Online only


    1300-1430

    Lecture Presentation (MS)

    MATHEMATICAL PRELIMINARIES

    Face to Face

    Lecture Presentation (MS)

    CLUSTERING

    Face to Face

    Lecture Presentation (MS)

    DIMENSIONALITY REDUCTION AND FEATURE EXTRACTION

    Face to Face

    Lecture Presentation (MS)

    CLASSIFICATION

    Face to Face

    Lecture  and  (MS)

    PREDICTIVE MODELS

    Face to Face

    Lecture Presentation (MS)

    NEURAL NETWORKS

    Face to Face

    1430 - 1500

    Break

    Break

    Break

    Break

    Break

    Break

    1500 - 1630

    Lecture Presentation (MS)

    MATHEMATICAL PRELIMINARIES

    Face to Face

    Lecture Presentation (MS)

    CLUSTERING

    Face to Face

    Lecture Presentation (MS)

    DIMENSIONALITY REDUCTION AND FEATURE EXTRACTION

    Face to Face

    Lecture Presentation (MS)

    CLASSIFICATION

    Face to Face

    Lecture Presentation (MS)

    PREDICTIVE MODELS

    Face to Face

    Lecture Presentation (MS)

    NEURAL NETWORKS

    Face to Face

    1630 - 1700

    open-discussion

    open-discussion

    open-discussion

    open-discussion

    open-discussion

    open-discussion


    0900 - 1030

    Working on your Assignment Presentation

    Assignment Presentation

    Face to Face Only

    Lecture and exercises  (AZ)

    Machine Learning (Supervised)

    Online only

    Lecture and exercises  (AZ)

    Machine Learning (Supervised)

    Online only

    1030 - 1100

    Break

    Break

    Break

    1100 - 1230

    Working on your Assignment Presentation

    Assignment Presentation

    Face to Face Only

    Lecture and exercises  (AZ)

    Machine Learning (Supervised)

    Online only

    Lecture and exercises  (AZ)

    Machine Learning (Clustering, PCA and Evaluation) (AZ)

    Online only

    Online quiz

    Workload

    The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

    12 half days
    Learning Activities Summary

    No information currently available.

  • Assessment

    The University's policy on Assessment for Coursework Programs is based on the following four principles:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. Assessment must maintain academic standards.

    Assessment Summary
    Assessment Task Task Type Individual / Group Due (week)* Weighting Learning Outcome
    Critical review Formative & Summative Group TBD 30%
    Online quiz Summative Individual TBD 20%
    Project Summative Individual TBD 50%

    * The specific due date for each assessment task will be available on MyUni.
    Assessment Detail
    Online quiz (20%)
    Critical review presentation (30%)
    Project (50%)
    Submission
    Critical litrature review: group presentation

    Project:  code and report submission  via Jupyter Notebook
    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.

    Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.

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

  • Student Feedback

    The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.

    SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.

  • Student Support
  • Policies & Guidelines
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