Decision Analysis 2

Faculty of Science and Technology
Department of Petroleum Engineering

Introduction

This course gives participants both the understanding and skills needed to build useful models in decision situations and to use them to draw powerful insights into the decision. The course topics range from conceptual, such as how to define the goodness of a model, to practical, such as best practices in laying out the structure of a model in Microsoft® Excel. In this highly interactive course, most of the lectures include hands-on experience for the participants to cement their grasp of new skills. In the exercises, participants build a complete decision model under the guidance of the instructor. The lectures will take place in a lecture room where the students have access to computers with Excel and other modelling applications. Because this workshop builds on the core course MPE630 - Decision Analysis I, we strongly encourage participants complete this course first. In addition, participants are expected to be familiar with but not necessarily experts in using Excel.

Learning outcome

After completing this course the student should be able to:

Contents

- Review of decision analysis basics.

- Planning the model.

- Best practices in decision modeling using Excel.

- Model development.

- Debugging models.

- Software tools for probabilistic analysis.

- Sensitivity and probabilistic analysis.

- Drawing and communicating insights.

Prerequisites

Applicants for single subjects need to meet the requirements for admission to the master programme in Petroleum Engineering.

The grade for the course will be based on tests (40%), exercises (50%) and class participation (10%). Class participation will be evaluated subjectively. As the instructor, I value attendance, punctuality, familiarity with the required readings, and classroom questions or comments that are relevant and insightful. Differences in technical background or skill are not a criterion. In general, I evaluate classroom participation on the basis of the extent to which you contribute to a positive and effective learning environment (for yourself and others). Demonstrating mastery of advanced topics at innappropriate times does not contribute to a positive learning environment. Correcting me when I make a mistake, however, or asking what may appear to be a naive question, quite often contribute positively. ("Dumb" questions, which rarely are that, are usually shared by many students, and asking one can keep the class on track.)

Because the course consists of continuous practical evaluation, no final exam is offered for this course. If a student fails the course or wants to improve the grade, she/he needs to take the course over again.

Only available to students in

- Bachelor level at the Faculty of Science and Technology.

- Master level at the Faculty of Science and Technology.

- PhD level at the Faculty of Science and Technology.

Lectures and exercises.

Bratvold, R.B and S.H. Begg: Decision-Making under Uncertainty, SPE, 2009.

McNamee, P. and Celona, J. 2005. Decision Analysis for the Professional, Fourth edition. Menlo Park, CA: SmartOrg, Inc.

Selected papers.