نمذجة جيوستاتيكية

MODULE DESCRIPTION FORM

نموذج وصف المادة الدراسية

Module Information معلومات المادة الدراسية
Module Title	Geostatistical Modelling				Module Delivery
Module Type	E				☒ Theory ☒ Lecture ☒ Lab ☐ Tutorial ☐ Practical ☐ Seminar
Module Code	BOG1172
ECTS Credits	6
SWL (hr/sem)	150
Module Level		UGx11 UGIV	Semester of Delivery				Seven
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

1. Give students the opportunity to acquire important statistical knowledge that can  be used to analyze spatial petrophysical estimation and modeling.

2. Make the students aware with the common workflow of incorporating Geostatistics in the overall reservoir characterization.  

3. Students will get the opportunity to be familiar with Petrel Software.

Module Learning Outcomes

مخرجات التعلم للمادة الدراسية

The learning outcomes for the described class content can be summarized as follows:

Understand the concept of spatial statistics and its importance in analyzing and interpreting spatial data.
Gain proficiency in variogram modeling and its role in quantifying spatial variability and correlation.
Learn about covariograms and correlograms as tools to analyze the spatial correlation between variables.
Develop an understanding of reservoir heterogeneity and its impact on spatial data analysis.
Recognize the concepts of isotropy and anisotropy and their significance in spatial statistics.
Acquire knowledge and skills in spatial prediction techniques such as nearest neighbor and inverse distance interpolation.
Master different kriging methods, including ordinary kriging, simple kriging, universal kriging, co-kriging, block kriging, and Bayesian kriging.
Gain practical experience in conducting spatial simulations and understanding their applications.
Learn cross-validation techniques to assess the accuracy and reliability of spatial predictions.
Develop proficiency in using the Petrel Software for analyzing real reservoir-petrophysical spatial data, including data visualization, interpolation, kriging, and simulation.

By the end of the course, students will have a solid foundation in spatial statistics principles and techniques, enabling them to effectively analyze and interpret spatial data in the context of reservoir engineering and petrophysics. They will also have gained hands-on experience using industry-standard software for spatial data analysis and visualization.

Indicative Contents

المحتويات الإرشادية

Geostatistics or petrophysical property spatial statistics is one of the fastest growing areas of statistics and has essential preliminary step in reservoir characterization and reservoir simulation. It mainly concerns modelling the data into 2D or 3D spatial surfaced distribution. The Geostatistics can be applied to track many problems in other disciplines such as hydrology, geography, water resources, waste management, forestry, oceanography, meteorology, agriculture, weather forecast, etc., and in general to every problem where data are observed at geographic locations.  

Learning and Teaching Strategies

استراتيجيات التعلم والتعليم

Strategies

The main strategy that will be adopted in delivering this module is to engage students actively in the learning process, fostering their participation and enhancing their critical thinking skills. This will be accomplished through a combination of interactive lectures, tutorials, and practical exercises that involve hands-on experience with spatial data analysis. Students will have the opportunity to work with real reservoir-petrophysical spatial data using the Petrel Software, allowing them to apply the concepts learned in class to real-world scenarios. Additionally, various sampling activities and experiments will be incorporated to make the learning experience more engaging and relevant to the students' interests. Through these activities, students will develop a deeper understanding of spatial statistics and its application in reservoir engineering, while honing their analytical and problem-solving abilities.

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

150

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	Introduction about spatial statistics
Week 2	Variogram modeling
Week 3	Covariogram & Correlogram
Week 4	Reservoir Heterogeneity
Week 5	Isotropy, anisotropy
Week 6	Spatial prediction: Nearest Neighbor and Inverse Distance interpolation
Week 7	Kriging: ordinary kriging, simple kriging, universal kriging, co-kriging, block kriging, and Bayesian Kriging
Week 8	Simulations
Week 9	Cross-validation
Week 10	Extensive use of the Petrel Software to analyze real reservoir-petrophysical spatial data
Week 11	Recap and problem-solving exercises
Week 12	Mid-semester examination (Week 7)
Week 13	Advanced applications and case studies
Week 14	Review and practice sessions
Week 15	Final exam preparation and course summary
Week 16	Preparation for final exams (no regular class)

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	1. Bivand, R. S., Pebesma, E. J., Gomez-Rubio, V., & Pebesma, E. J. (2008). Applied spatial data analysis with R (Vol. 747248717). New York: Springer.   2. Journel, A. G. (1989) Fundamentals of geostatistics in five lessons. In Short Course in Geology, Vol. 8. American Geophysical Union, Washington, DC, 40 pp.
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.

نمذجة جيوستاتيكية

Geostatistical Modelling

E

BOG1172

6

150