تحليل بيانات نفطية

MODULE DESCRIPTION FORM

نموذج وصف المادة الدراسية

Module Information معلومات المادة الدراسية
Module Title	Petroleum Data Analytics				Module Delivery
Module Type	S				☒ Theory ☒ Lecture ☒ Lab ☐ Tutorial ☐ Practical ☐ Seminar
Module Code	BOG1155
ECTS Credits	4
SWL (hr/sem)	100
Module Level		UGx11 UGIII	Semester of Delivery				Five
Administering Department		Oil and Gas Engineering	College	Oil and Gas Engineering
Module Leader			e-mail
Module Leader’s Acad. Title			Module Leader’s Qualification
Module Tutor			e-mail
Peer Reviewer Name			e-mail
Scientific Committee Approval Date			Version Number			1.0

Relation with other Modules

العلاقة مع المواد الدراسية الأخرى

Prerequisite module

Semester

Co-requisites module

Semester

Module Aims, Learning Outcomes and Indicative Contents

أهداف المادة الدراسية ونتائج التعلم والمحتويات الإرشادية

Module Objectives

أهداف المادة الدراسية

The main objectives of the class "Introduction to Elementary Probability Theory and its Applications in Engineering and Sciences" are as follows:

Understand the fundamental concepts of probability theory and its relevance in engineering and sciences.
Develop knowledge and proficiency in analyzing discrete and continuous probability distributions.
Learn techniques for parameter estimation in probabilistic models.
Gain an understanding of hypothesis testing and its application in earth sciences and engineering.
Apply probability theory to real-world engineering and scientific problems, enhancing problem-solving skills.
Develop critical thinking and analytical reasoning abilities through the application of probability theory.
Gain practical skills in utilizing probability theory for decision-making in engineering and scientific contexts.
Develop a strong foundation in probability theory as a basis for further study in advanced engineering and scientific fields.

By achieving these objectives, students will be equipped with the necessary knowledge and skills to apply probability theory effectively in various engineering and scientific applications.

Module Learning Outcomes

مخرجات التعلم للمادة الدراسية

Upon completion of the class, students will be able to:

Understand the fundamental concepts of probability theory and its significance in petroleum engineering applications.
Apply probability theory to analyze and solve problems specific to the petroleum engineering field, such as reservoir modeling, production forecasting, and risk assessment.
Differentiate between discrete and continuous probability distributions and select appropriate distributions for modeling uncertainties in petroleum engineering.
Utilize statistical techniques to estimate parameters of probability distributions for reservoir characterization and production data analysis.
Perform hypothesis testing in the context of petroleum engineering to make informed decisions regarding reservoir behavior and production performance.
Apply probability theory to assess uncertainty and risk in petroleum engineering projects, including reserve estimation and economic evaluations.
Demonstrate proficiency in utilizing probability models and distributions to simulate and optimize petroleum engineering processes and operations.
Develop critical thinking skills to evaluate the reliability and uncertainty associated with petroleum engineering data and interpretations using probability theory.
Communicate effectively about probability-related concepts, analysis results, and findings in written reports and oral presentations specific to petroleum engineering applications.
Recognize the applications and limitations of probability theory in various areas of petroleum engineering, such as reservoir management, production optimization, and decision-making under uncertainty.

By achieving these learning outcomes, petroleum engineering students will have a solid foundation in elementary probability theory and its practical applications, equipping them with the necessary skills to tackle uncertainties and make informed decisions in the petroleum industry.

Indicative Contents

المحتويات الإرشادية

Introduction to elementary probability theory and its applications in engineering and sciences; discrete and continuous probability distributions; parameter estimation; hypothesis earth sciences and engineering.

Learning and Teaching Strategies

استراتيجيات التعلم والتعليم

Strategies

Type something like: The main strategy that will be adopted in delivering this module is to encourage students’ participation in the exercises, while at the same time refining and expanding their critical thinking skills. This will be achieved through classes, interactive tutorials and by considering types of simple experiments involving some sampling activities that are interesting to the students.

Student Workload (SWL)

الحمل الدراسي للطالب محسوب لـ ١٥ اسبوعا

Structured SWL (h/sem)

الحمل الدراسي المنتظم للطالب خلال الفصل

Structured SWL (h/w)

الحمل الدراسي المنتظم للطالب أسبوعيا

Unstructured SWL (h/sem)

الحمل الدراسي غير المنتظم للطالب خلال الفصل

Unstructured SWL (h/w)

الحمل الدراسي غير المنتظم للطالب أسبوعيا

Total SWL (h/sem)

الحمل الدراسي الكلي للطالب خلال الفصل

100

Module Evaluation تقييم المادة الدراسية
As		Time/Number	Weight (Marks)	Week Due	Relevant Learning Outcome
Formative assessment	Quizzes		10% (10)	5 and 10	LO #1, #2 and #10, #11
	Assignments		10% (10)	2 and 12	LO #3, #4 and #6, #7
	Projects / Lab.		10% (10)	Continuous	All
	Report		10% (10)	13	LO #5, #8 and #10
Summative assessment	Midterm Exam	1hr	10% (10)	7	LO #1 - #7
Summative assessment	Final Exam	2hr	50% (50)	16	All
Total assessment			100% (100 Marks)

Delivery Plan (Weekly Syllabus) المنهاج الاسبوعي النظري
Week	Material Covered
Week 1	Definition of Statistics
Week 2	Univariate Statistical Analysis
Week 3	Statistical data representation
Week 4	Types of data distribution
Week 5	Cumulative Probability Function
Week 6	Bivariate Statistical Analysis
Week 7	Introduction to Multivariate Statistical Analysis
Week 8	Regression Analysis (statistical modeling)
Week 9	Statistical validation tools
Week 10	Modeling and prediction
Week 11	Applications of R
Week 12	Recap and problem-solving exercises
Week 13	Final exam preparation and course summary
Week 14	Preparation for final exams (no regular class)
Week 15
Week 16	Preparatory week before the final Exam

Delivery Plan (Weekly Lab. Syllabus) المنهاج الاسبوعي للمختبر
Week	Material Covered
Week 1
Week 2
Week 3
Week 4
Week 5
Week 6
Week 7

Learning and Teaching Resources مصادر التعلم والتدريس
	Text	Available in the Library?
Required Texts
Recommended Texts	Title: "Introduction to Statistical Learning: with Applications in R" Authors: Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani Publisher: Springer ISBN: 978-1461471370
Websites

Grading Scheme مخطط الدرجات
Group	Grade	التقدير	Marks %	Definition
Success Group (50 - 100)	A - Excellent	امتياز	90 - 100	Outstanding Performance
	B - Very Good	جيد جدا	80 - 89	Above average with some errors
	C - Good	جيد	70 - 79	Sound work with notable errors
	D - Satisfactory	متوسط	60 - 69	Fair but with major shortcomings
	E - Sufficient	مقبول	50 - 59	Work meets minimum criteria
Fail Group (0 – 49)	FX – Fail	راسب (قيد المعالجة)	(45-49)	More work required but credit awarded
Fail Group (0 – 49)	F – Fail	راسب	(0-44)	Considerable amount of work required

Note: Marks Decimal places above or below 0.5 will be rounded to the higher or lower full mark (for example a mark of 54.5 will be rounded to 55, whereas a mark of 54.4 will be rounded to 54. The University has a policy NOT to condone "near-pass fails" so the only adjustment to marks awarded by the original marker(s) will be the automatic rounding outlined above.

تحليل بيانات نفطية

Petroleum Data Analytics

S

BOG1155

4

100