PETROENG 4064 - Data analytics in oil and gas industry
North Terrace Campus - Semester 2 - 2024
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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 Assessment Exam, assignment, project Course Staff
Course Coordinator: Associate Professor Abbas Zeinijahromi
Course Timetable
The full timetable of all activities for this course can be accessed from .
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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 . The course develops the following EA Elements of Competency to levels of introductory (A), intermediate (B), advanced (C):
1.1 1.2 1.3 1.4 1.5 1.6 2.1 2.2 2.3 2.4 3.1 3.2 3.3 3.4 3.5 3.6 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 daysLearning Activities Summary
No information currently available.
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Assessment
The University's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- 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 NotebookCourse 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 .
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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.
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Student Support
- Academic Integrity for Students
- Academic Support with Maths
- Academic Support with writing and study skills
- Careers Services
- Library Services for Students
- LinkedIn Learning
- Student Life Counselling Support - Personal counselling for issues affecting study
- Students with a Disability - Alternative academic arrangements
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Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
- Academic Credit Arrangements Policy
- Academic Integrity Policy
- Academic Progress by Coursework Students Policy
- Assessment for Coursework Programs Policy
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- Intellectual Property Policy
- IT Acceptable Use and Security Policy
- Modified Arrangements for Coursework Assessment Policy
- Reasonable Adjustments to Learning, Teaching & Assessment for Students with a Disability Policy
- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process
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