PE6503 - DRILLING FLUIDS AND CEMENTING TECHNIQUES (Syllabus) 2013-regulation Anna University

PE6503 - DRILLING FLUIDS AND CEMENTING TECHNIQUES (Syllabus) 2013-regulation Anna University

PE6503

DRILLING FLUIDS AND CEMENTING TECHNIQUES

 LPTC
3003

OBJECTIVES:
To enable the students to understand the types of drilling fluids and cementing techniques

UNIT I

9
Introduction to the basic functions and properties of drilling fluids and cement slurries. Compositions and related properties of drilling fluids and cement slurries.

UNIT II

9
Drilling fluids – classification – water base drilling fluids. Testing of drilling fluids. Drilling fluid additives.


UNIT III

9
Types of equipment and methods used in cementing operations. Drilling fluid and cement slurry hydraulics.

UNIT IV

9
Determination of torque and drag. Calculation of cutting transport efficiency. Placement technique of cements. Gas migration through cement columns.

UNIT V

9
Will cementing – chemistry of cements. Cementing principles – primary cementing, secondary cementing, linear cementing, plug cementing, and single stage cementing, multistage casing cementing.

TOTAL : 45 PERIODS

OUTCOMES: Upon completion of this course, the students would have
• Learned the concepts and applications of drilling fluids
• Learned the equipments involved in the cementing operations

TEXT BOOKS:
1. Rabia.H. ‘Oil Well Drilling Engineering, Principles And Practices’ Graham And Trotman Ltd. 1985.
2. Smith.P.K, ’Cementing’ SPE Pulications 2nd Edition 1976.
3. Cementing Technology – Powel Schlumberger Publication 1984.

REFERENCES
1. Mc.Cray. A.W and Cole.F.W. ‘Oil Well Drilling Technology’ University of Oklahoma Press, Norman 1959.
2. Standard Handbook of petroleum and Natural Gas Engineering. 2nd Edition. William C Lyons, Gary C Plisga. Gulf Profession.
Previous
Next

Comments

Post a Comment

Popular posts from this blog

CS3491 Syllabus - Artificial Intelligence And Machine Learning - 2021 Regulation Anna University

CS3491 Syllabus - Artificial Intelligence And Machine Learning - 2021 Regulation Anna University CS3491 ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING  LTPC 3024 COURSE OBJECTIVES: • The main objectives of this course are to: • Study about uninformed and Heuristic search techniques. • Learn techniques for reasoning under uncertainty • Introduce Machine Learning and supervised learning algorithms • Study about ensembling and unsupervised learning algorithms • Learn the basics of deep learning using neural networks We're excited to announce the launch of our new website! Visit NameWheelSpinner.com to explore its features and benefits. UNIT I PROBLEM SOLVING 9 Introduction to AI - AI Applications - Problem solving agents – search algorithms – uninformed
Read more

BE3251 - Basic Electrical and Electronics Engineering (Syllabus) 2021-regulation Anna University

BE3251 - Basic Electrical and Electronics Engineering (Syllabus) 2021-regulation Anna University BE3251 Basic Electrical and Electronics Engineering  LPTC 3003 COURSE OBJECTIVES: • To introduce the basics of electric circuits and analysis • To impart knowledge in the basics of working principles and application of electrical machines • To introduce analog devices and their characteristics • To educate on the fundamental concepts of digital electronics • To introduce the functional elements and working of measuring instruments We're excited to announce the launch of our new website! Visit NameWheelSpinner.com to explore its features and benefits. UNIT I ELECTRICAL CIRCUITS 9 DC Circuits: Circuit Components: Conductor, Resistor, Inductor, Capacitor – Ohm’s Law - Kirchhoff’s Laws –Independent and Dependent Sources – Simple problems- Nodal Analysis, Mesh analysis with Independent sources only (Steady state) Introd
Read more

CS3401 Syllabus - Algorithms - 2021 Regulation Anna University

CS3401 Syllabus - Algorithms - 2021 Regulation Anna University CS3401 ALGORITHMS  LTPC 3024 COURSE OBJECTIVES: • To understand and apply the algorithm analysis techniques on searching and sorting algorithms • To critically analyze the efficiency of graph algorithms • To understand different algorithm design techniques • To solve programming problems using state space tree • To understand the concepts behind NP Completeness, Approximation algorithms and randomized algorithms. We're excited to announce the launch of our new website! Visit NameWheelSpinner.com to explore its features and benefits. UNIT I INTRODUCTION 9 Algorithm analysis: Time and space complexity - Asymptotic Notations and its properties Best case, Worst case and average case analys
Read more