Review of Expected Operations

Similar documents
Random Variables and Applications OPRE 6301

AP Statistics Chapter 6 - Random Variables

Economic Risk and Decision Analysis for Oil and Gas Industry CE School of Engineering and Technology Asian Institute of Technology

Learning Objectives = = where X i is the i t h outcome of a decision, p i is the probability of the i t h

Key concepts: Certainty Equivalent and Risk Premium

Chapter 7: Random Variables and Discrete Probability Distributions

Economic Risk and Decision Analysis for Oil and Gas Industry CE School of Engineering and Technology Asian Institute of Technology

Sensitivity Analysis with Data Tables. 10% annual interest now =$110 one year later. 10% annual interest now =$121 one year later

Decision Theory Using Probabilities, MV, EMV, EVPI and Other Techniques

HHH HHT HTH THH HTT THT TTH TTT

Chapter 16. Random Variables. Copyright 2010 Pearson Education, Inc.

SCHOOL OF BUSINESS, ECONOMICS AND MANAGEMENT. BF360 Operations Research

Probability is the tool used for anticipating what the distribution of data should look like under a given model.

Random variables. Discrete random variables. Continuous random variables.

DECISION ANALYSIS. Decision often must be made in uncertain environments. Examples:

Chapter 16. Random Variables. Copyright 2010, 2007, 2004 Pearson Education, Inc.

Dr. Abdallah Abdallah Fall Term 2014

STA Module 3B Discrete Random Variables

Making Hard Decision. ENCE 627 Decision Analysis for Engineering. Identify the decision situation and understand objectives. Identify alternatives

Basic Principles of Probability and Statistics. Lecture notes for PET 472 Spring 2012 Prepared by: Thomas W. Engler, Ph.D., P.E

Econ 6900: Statistical Problems. Instructor: Yogesh Uppal

Review of the Topics for Midterm I

Chapter 7: Random Variables

STA Rev. F Learning Objectives. What is a Random Variable? Module 5 Discrete Random Variables

MS-E2114 Investment Science Lecture 5: Mean-variance portfolio theory

19 Decision Making. Expected Monetary Value Expected Opportunity Loss Return-to-Risk Ratio Decision Making with Sample Information

Learning Objectives 6/2/18. Some keys from yesterday

MBF1413 Quantitative Methods

Statistics for Managers Using Microsoft Excel 7 th Edition

Decision Analysis. Introduction. Job Counseling

Decision Analysis under Uncertainty. Christopher Grigoriou Executive MBA/HEC Lausanne

Concave utility functions

Project Risk Analysis and Management Exercises (Part II, Chapters 6, 7)

Chapter 13 Decision Analysis

Models & Decision with Financial Applications Unit 3: Utility Function and Risk Attitude

Decision Analysis CHAPTER LEARNING OBJECTIVES CHAPTER OUTLINE. After completing this chapter, students will be able to:

Econ 422 Eric Zivot Summer 2004 Final Exam Solutions

Decision Making Under Risk Probability Historical Data (relative frequency) (e.g Insurance) Cause and Effect Models (e.g.

Module 15 July 28, 2014

Causes of Poor Decisions

Managerial Economics Uncertainty

5.7 Probability Distributions and Variance

PROBABILITY AND STATISTICS CHAPTER 4 NOTES DISCRETE PROBABILITY DISTRIBUTIONS

Lecture 9: Plinko Probabilities, Part III Random Variables, Expected Values and Variances

DECISION ANALYSIS. (Hillier & Lieberman Introduction to Operations Research, 8 th edition)

The Central Limit Theorem. Sec. 8.2: The Random Variable. it s Distribution. it s Distribution

Notes 10: Risk and Uncertainty

Random Variables. Copyright 2009 Pearson Education, Inc.

Statistical Methods in Practice STAT/MATH 3379

MA 1125 Lecture 14 - Expected Values. Wednesday, October 4, Objectives: Introduce expected values.

variance risk Alice & Bob are gambling (again). X = Alice s gain per flip: E[X] = Time passes... Alice (yawning) says let s raise the stakes

Objective of Decision Analysis. Determine an optimal decision under uncertain future events

Week 7. Texas A& M University. Department of Mathematics Texas A& M University, College Station Section 3.2, 3.3 and 3.4

Chapter 3. Decision Analysis. Learning Objectives

Mean of a Discrete Random variable. Suppose that X is a discrete random variable whose distribution is : :

Statistics (This summary is for chapters 17, 28, 29 and section G of chapter 19)

Chapter 18 Student Lecture Notes 18-1

Counting Basics. Venn diagrams

Statistics 6 th Edition

UEP USER GUIDE. Preface. Contents

Examples: Random Variables. Discrete and Continuous Random Variables. Probability Distributions

Gamma. The finite-difference formula for gamma is

Project Risk Evaluation and Management Exercises (Part II, Chapters 4, 5, 6 and 7)

Discrete probability distributions

Market Liquidity and Performance Monitoring The main idea The sequence of events: Technology and information

Chapter 5 Discrete Probability Distributions. Random Variables Discrete Probability Distributions Expected Value and Variance

VIDEO 1. A random variable is a quantity whose value depends on chance, for example, the outcome when a die is rolled.

Statistics for Business and Economics: Random Variables (1)

UNIT 5 DECISION MAKING

Managerial Economics

Week 1 Quantitative Analysis of Financial Markets Basic Statistics A

Subject : Computer Science. Paper: Machine Learning. Module: Decision Theory and Bayesian Decision Theory. Module No: CS/ML/10.

STATISTICS and PROBABILITY

MATH 118 Class Notes For Chapter 5 By: Maan Omran

Test 7A AP Statistics Name: Directions: Work on these sheets.

Problems from 9th edition of Probability and Statistical Inference by Hogg, Tanis and Zimmerman:

Indexing and Price Informativeness

Decision Making. DKSharma

Discrete Random Variables

THE UNIVERSITY OF TEXAS AT AUSTIN Department of Information, Risk, and Operations Management

Binomial Random Variables. Binomial Random Variables

Chapter 6: Random Variables

Simulation Wrap-up, Statistics COS 323

A B C D E F 1 PAYOFF TABLE 2. States of Nature

Introduction to Statistics I

Business Statistics Midterm Exam Fall 2013 Russell

Chapter 7 1. Random Variables

Statistics & Flood Frequency Chapter 3. Dr. Philip B. Bedient

TOPIC: PROBABILITY DISTRIBUTIONS

Asymmetric Information: Walrasian Equilibria, and Rational Expectations Equilibria

Unit 04 Review. Probability Rules

Learning Objec0ves. Statistics for Business and Economics. Discrete Probability Distribu0ons

Math 101, Basic Algebra Author: Debra Griffin

The Islamic University of Gaza Faculty of Commerce Quantitative Analysis - Prof. Dr. Samir Safi Midterm #1-15/3/2015. Name

Chapter 7. Random Variables

Shifting our focus. We were studying statistics (data, displays, sampling...) The next few lectures focus on probability (randomness) Why?

Simple Random Sample

Lecture 9. Probability Distributions. Outline. Outline

Part 1 In which we meet the law of averages. The Law of Averages. The Expected Value & The Standard Error. Where Are We Going?

Standard Normal, Inverse Normal and Sampling Distributions

Transcription:

Economic Risk and Decision Analysis for Oil and Gas Industry CE81.98 School of Engineering and Technology Asian Institute of Technology January Semester Presented by Dr. Thitisak Boonpramote Department of Mining and Petroleum Engineering, Chulalongkorn University Review of Expected Operations 1

Expected Value of Random Variable Expected value or mean Discrete: E { X } = xip( x i ) where E{X} = expectation operator, read expectation of P(x i ) = P(X=x i ), unconditional probability associated with variable x E(x) often referred to as mean of X n i= 1 Continuous: E ( X ) = xf ( x ) dx = μ X Variance Variance: the sum of squared deviations about the population mean. Var(X)= E{[X-E(X)] }= E(X)-[E(X)] Discrete: where S {X} = variance of X Continuous: s s n { X } = ( xi E{ X }) P( x i ) i= 1 { X } = s { X } Var ( X) [ x E( X)] f ( x) dx = x f ( x) dx μx =

Multiplication of a random variable Multiplication of a random variable by a constant Expected value Variance E ( cx ) = ce ( X ) Var ( cx ) = c Var ( X ) Multiplication of a random variable Multiplication of two independent random variables Expected value Variance E ( XY ) = E ( X ) E ( Y ) Var ( XY ) = Var ( X )[ E ( Y )] + Var ( Y )[ E ( X )] + Var ( X ) Var ( Y ) 3

Sums of a random variable Addition of two independent random variables Expected value E{X + Y} = E{X} + E{Y} Variance s {X + Y} = s {X} + s {Y} Sums of a random variable Linear combination of two or more independent variables. Expected value Variance E ( c1 X + cy ) = c1e( X ) + ce( Y ) Var ( c1 X + c Y ) = c1 Var( X ) + cvar( Y ) 4

Example Calculation of Expected Value Expected results from drilling prospect 3% chance of MSTB 5% chance of 6 MSTB % chance of 95 MSTB What are mean, variance, and standard deviation of expected reserves? Example Calculation of Expected Value Probability, p i Reserves, X i, MSTB E{X} = p i X i (X i - E{X}) Variance p i (Xi- E{X}).3 6. 1,5. 367.5.5 6 3. 5. 1.5. 95 19. 1,6. 3. 1. 55. 7. 5

Example Calculation of Expected Value Mean, or expected value, of reserves 55. MSTB Variance 7. MSTB Standard deviation 6.5 MSTB ( 7) Interpretation Over large number of similar trials, expect to recover 55 MSTB with 68% confidence result will lie between 8.5 (55-6.5) and 81.5 (55+6.5) MSTB The Standard Normal Curve 6

Expected Value Concept Expected Monetary Value When random variable in expected value is monetary value, calculated expected value called expected monetary value, EMV EMV is weighted average of possible monetary values (usually NPV s), weighted by respective probabilities Monetary values can be undiscounted or undiscounted EMV of NPV s called expected present value profit 7

Structural Elements in EMV Calculations Outcome state probabilities, P(S i ): probabilities assigned to outcome states Criterion: Basis decision makers use for most appropriate course of action from among the alternatives Two ways to display structural elements Payoff table (tabular) Decision tree (graphical) EMV Example If you spend $5, drilling a wildcat oil well, geologists estimate: the probability of a dry hole is.6 with a probability of.3 that the well will be a producer can be sold immediately for $,, and a probability of.1 that the well will produce at a rate that will generate a $1,, immediately sale value. What is the project expected value? 8

EMV Example (Con d) Let P= probability of success, 1-P = probability of failure Expected Value = Expected profits-expected Costs = (P)(Income-Cost)-(1-P)(Cost) =.3(,-5)+.1(1,-5)-.6(5) = $ What does expected value of $, means? -5% chance that you ll get $, on your investment -the most probable outcome of selecting an alternative -other interpretation Interpretation of Expected Value What EV is not: Not the most probable outcome of selecting an alternative Not the number which we expect to equal or exceed 5% of the time Expected value is average value per decision realized when the alternative is repeated over many trials If the expected value concept is used on rare occasions, it becomes the same as a one-time bet in the casino. The concept of expected value represents a play-theaverage strategy. It requires consistently applied to all project evaluations over a long period. 9

Interpretation of Expected Value The $, expected value is a statistical long-term average profit or loss that will be realized over many repeated investments of this type. It never guarantee this value over any individual try. It means if we drilled a large number of well say 1 wells of the type described, we expect statistics to begin to work out, we would expect about 6 dry holes out of 1 wells with about 3 wells producing a $,, income and about 1 wells producing a $1,, income. This make total income of $7,, from 1 wells drilled costing a total of $5,, leaving total profit of $,, after the costs, or profit per well of $,, which is the expected value result of the example. Example 1: Drill vs. Farm out Outcomes likely for drilling prospect Dry hole probability 65%, loss $5, Successful well probability 35%, NPV of future net revenues $5, Can farm out prospect, remove exposure to drilling expenditure, retain overriding royalty interest, NPV $5, Determine whether to drill or farm out 1

Example 1: Payoff Table Application Outcome Drill Farm Out State Probabililty NPV, M$ EMV, M$ NPV, M$ EMV, M$ Dry hole.65-5 -16.5 Producer.35 +5 +175 5 17.5 1. 1.5 17.5 Example 1: Payoff Table Application Since EMV of farm-out ($17.5M)>EMV of drilling, we should farm out Result highly sensitive to probability of producer If increased from 35 to 36%, drillling better option Sensitivity analysis useful if unsure about probabilities Variance of drill option much greater than variance of farm-out option (drilling much more risky) 11

Example 1: Sensitivity Analysis on Probabilities Probabilities used in EMV analysis usually most uncertain parameters We need to determine influence of changes in probabilities on apparent optimal decision to improve our decision making Consider example with two acts (drill or farm out) and two events (dry hole or producer) Example 1: Sensitivity Analysis on Probabilities Outcome State Probabililty NPV, M$ Drill EMV, M$ NPV, M$ Farm Out EMV, M$ Dry hole.65-5 -16.5 Producer.35 +5 +175 5 17.5 1. 1.5 17.5 1

Example 1: Sensitivity Analysis on Probabilities Let p = probability of dry hole (1-p) = probability of producer Then EV{drill} EV{farmout} = p(-5) + (1-p)(5) = -75p + 5 = p() + (1-p)(5) = -5p +5 Example Sensitivity Analysis on Probabilities Decision maker indifferent if EV of two alternatives equal Probability at point of indifference given by EMV{Drill} = EMV {Farmout} -75p + 5 = -5p +5 p =.649 or 64.9% Farm-out optimal for p>.649, but results highly sensitive to change in probability We must do our best to ensure probability correct 13

Example 1: Sensitivity Analysis on Probabilities Example : Acreage acquisition A company with 1 acres leased wants to drill a well on 16-acre prospect area We can join unit by leasing remaining 6 acres in unit Evaluation assumes we acquire acreage Gross well cost (with equipment) = $11M Gross dry hole cost = $8M 14

Example : Payoff Table Application We have identified 3 options and determined NPV s for several outcomes Participate in drilling with 37.5% non-operating WI (6/16x1 = 37.5%) Farm out acreage and retain 1/8-th of 7/8-th s royalty interest on 6 acres Be carried with back-in privilege (37.5% WI) after investing parties have recovered 15% of investment Example : Payoff Table Application Outcomes Dry hole MSTB 35 MSTB 5 MSTB 65 MSTB Probability.5.3.5.15.5 Drill with 37.5% WI -3 4.357 45.448 87.411 15.863 Net Present Value, M$ Farm out Retain ORI 8.733 14.646.693 6.41 37.5% Back-in.75 34.14 73.71 111.141 15

Example : Payoff Table Application Answer questions Should we lease adjacent land (mineral rights)? If so, what maximum amount should we pay? If we lease adjacent land, which option will be most valuable to us? Example : Payoff Table Application Probability Outcome State Drill with 37.5% NPV EMV Farm out with ORI NPV EMV Back in with 37.5% NPV EMV Dry hole.5-3. -7.5 MSTB.3 4.357 1.37 8.733.6.75.5 35 MSTB.5 45.448 11.36 14.646 3.66 34.14 8.536 5 MSTB.15 87.411 13.11.693 3.14 73.71 11.57 65 MSTB.5 15.863 6.93 6.41 1.3 111.141 5.557 EMV, M$ 4.574 1.76 5.375 Standard deviation, M$ 45.6 7.89 3.869 16

Example : Payoff Table Application Back-in has largest EMV and is best option Maximum value of additional acreage is $5,375/6 = $43 per acre If acreage acquired for exactly $5,375, rate of return will be 1% (discount rate used to determine NPV s) Rate of return increases as we pay less to lease land Example : Sensitivity Analysis on Probabilities For the example with three alternatives (drill, farm out, back-in), graphical method easier to implement 17

Example : Expected Opportunity Loss Definition: EOL is difference between actual profit or loss and profit or loss that would have resulted if decision maker had had perfect information at time decision made Example: choose to drill well, turns out to be dry hole, lose $3M Farm-out would have had zero loss EOL = $3M = $3M Expected Opportunity Loss EOL minimization rule can be used in place of EMV maximization rule as basis for decision making Result same with either rule EMV easier to work with in complex situations 18

Example Expected Opportunity Loss Analyze data in following table using EOL criterion (for drill, farm-out, back-in alternatives) Net Present Value, M$ Outcomes Probability Drill with 37.5% WI Farm out Retain ORI 37.5% Back-in Dry hole.5-3 MSTB.3 4.357 8.733.75 35 MSTB.5 45.448 14.646 34.14 5 MSTB.15 87.411.693 73.71 65 MSTB.5 15.863 6.41 111.141 Example Expected Opportunity Loss Construct opportunity loss table Identify maximum value entry in each row in previous table Subtract each entry in same row from maximum value Compute expected values by multiplying probabilities of outcomes by conditional opportunity losses Results in following table 19

Example Expected Opportunity Loss Outcome State Probability Drill with 37.5% OL, M$ EOL, M$ Farm out with ORI OL, M$ EOL, M$ Back-in with 37.5% OL, M$ EOL, M$ Dry hole.5 3 7.5 MSTB.3 4.376 1.31 7.983.395 35 MSTB.5 3.8 7.71 11.31.85 5 MSTB.15 66.718 1.8 13.699.55 65 MSTB.5 99.46 4.973 14.7.736 1. 8.81.68 8.11 Example Expected Opportunity Loss Best choice is back-in alternative, which has minimum EOL Same decision as with maximum EMV criterion

Summary of Decision Criteria Choose alternative with largest EMV when profit is payoff variable and alternatives are mutually exclusive Choose alternative with smallest EOL when cost is payoff variable and alternatives are mutually exclusive 1