OIL AND GAS RESERVES ESTIMATING WE HAVE MET THE ENEMY, AND HE IS US Peter R. Rose Senior Associate, Rose & Associates, LLP., and President AAPG Austin,

Slides:



Advertisements
Similar presentations
Chapter 7 Hypothesis Testing
Advertisements

Partnership Liquidation And Incorporation; Joint Ventures
1 Decision Making A General Overview 10th ed.. 2 Why study decision making? -It is the most fundamental task performed by managers. -It is the underlying.
McGraw-Hill/Irwin Copyright © 2007 by The McGraw-Hill Companies, Inc. All rights reserved. 5S Decision Theory.
Sensitivity Analysis In deterministic analysis, single fixed values (typically, mean values) of representative samples or strength parameters or slope.
Review You run a t-test and get a result of t = 0.5. What is your conclusion? Reject the null hypothesis because t is bigger than expected by chance Reject.
Statistical Issues in Research Planning and Evaluation
©2010 Prentice Hall Business Publishing, Auditing 13/e, Arens//Elder/Beasley Audit Sampling for Tests of Controls and Substantive Tests of Transactions.
4. Project Investment Decision-Making
Engineering Economic Analysis Canadian Edition
Chapter 19: Confidence Intervals for Proportions
11 Populations and Samples.
BPS - 3rd Ed. Chapter 131 Confidence intervals: the basics.
Engineering Systems Analysis for Design Richard de Neufville © Massachusetts Institute of Technology Flaw of Averages Slide 1 of 29 Richard de Neufville.
Introduction to the design (and analysis) of experiments James M. Curran Department of Statistics, University of Auckland
AM Recitation 2/10/11.
Introduction to Hypothesis Testing for μ Research Problem: Infant Touch Intervention Designed to increase child growth/weight Weight at age 2: Known population:
Determining Sample Size
Research Methodology Lecture No :16
Unpacking the Standards for Mathematical Practice Professional Development September 16, 2013.
Decision Trees and Influence Diagrams Dr. Ayham Jaaron.
+ The Practice of Statistics, 4 th edition – For AP* STARNES, YATES, MOORE Chapter 8: Estimating with Confidence Section 8.1 Confidence Intervals: The.
Population All members of a set which have a given characteristic. Population Data Data associated with a certain population. Population Parameter A measure.
Hypothesis Testing Quantitative Methods in HPELS 440:210.
Slide Slide 1 Chapter 8 Hypothesis Testing 8-1 Overview 8-2 Basics of Hypothesis Testing 8-3 Testing a Claim about a Proportion 8-4 Testing a Claim About.
Chapter 8 Introduction to Hypothesis Testing
SPE DISTINGUISHED LECTURER SERIES is funded principally through a grant of the SPE FOUNDATION The Society gratefully acknowledges those companies that.
A Review & Classification of Petroleum Resource Base in Thailand Anon Punnahitanon Petroleum Engineer Mineral Fuel Division Department of Mineral Resources.
Instructor Resource Chapter 5 Copyright © Scott B. Patten, Permission granted for classroom use with Epidemiology for Canadian Students: Principles,
90288 – Select a Sample and Make Inferences from Data The Mayor’s Claim.
Essential Statistics Chapter 131 Introduction to Inference.
90288 – Select a Sample and Make Inferences from Data The Mayor’s Claim.
Portfolio Management Unit – II Session No. 13 Topic: Introduction to Asset Allocation Unit – II Session No. 13 Topic: Introduction to Asset Allocation.
1 1 Slide Decision Theory Professor Ahmadi. 2 2 Slide Learning Objectives n Structuring the decision problem and decision trees n Types of decision making.
Engineering Economic Analysis Canadian Edition
Geo597 Geostatistics Ch9 Random Function Models.
Audit Sampling: An Overview and Application to Tests of Controls
Session Objectives To revisit the Audit Risk Model and Materiality concepts; To explain the Theory of Sampling as applied to audit To Explain the link.
Lecture 16 Section 8.1 Objectives: Testing Statistical Hypotheses − Stating hypotheses statements − Type I and II errors − Conducting a hypothesis test.
Section 10.1 Confidence Intervals
BPS - 3rd Ed. Chapter 131 Confidence Intervals: The Basics.
OIL AND GAS EVALUATIONS PROBABLE & POSSIBLE RESERVES WHAT WILL THE INVESTOR THINK? February 15, 2010 FORREST A. GARB & ASSOCIATES, INC. INTERNATIONAL PETROLEUM.
Copyright © 2008 by the American Academy of Actuaries September 2008 Reserve Ranges – A Summary of the COPLFR Issue Brief Marc F. Oberholtzer, FCAS, MAAA.
+ DO NOW. + Chapter 8 Estimating with Confidence 8.1Confidence Intervals: The Basics 8.2Estimating a Population Proportion 8.3Estimating a Population.
12/4/2015 Vijit Mittal (NBS, Gr. Noida) 1 Monte Carlo Simulation,Real Options and Decision Tree.
Engineering Systems Analysis for Design Richard de Neufville  Massachusetts Institute of Technology Use of Simulation Slide 1 of 17 Use of Simulation.
Decision Theory McGraw-Hill/Irwin Copyright © 2012 by The McGraw-Hill Companies, Inc. All rights reserved.
1 Decision Making A General Overview 10th ed.. 2 Why study decision making? -It is the most fundamental task performed by managers. -It is the underlying.
Managing Portfolio for Individual Investors Jakub Karnowski, CFA Portfolio Management for Financial Advisers.
Confidence Intervals and Hypothesis Testing Using and.
Sample Size Determination
AGEC 407 Risk Goals: 1.Convey an understanding of what is meant by risk 2.Describe the different types and sources of risk in agricultural production 3.Demonstrate.
Copyright © 2008 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Chapter 6 The Standard Deviation as a Ruler and the Normal Model.
Portfolio Management Unit – III Session No. 20 Topic: Tools for Formulating Capital Market Expectations Unit – III Session No. 20 Topic: Tools for Formulating.
Reserve Ranges Roger M. Hayne, FCAS, MAAA C.K. “Stan” Khury, FCAS, MAAA Robert F. Wolf, FCAS, MAAA 2005 CAS Spring Meeting.
Decision Theory Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill.
“ Building Strong “ Delivering Integrated, Sustainable, Water Resources Solutions Uncertainty & Variability Charles Yoe, Ph.D.
+ The Practice of Statistics, 4 th edition – For AP* STARNES, YATES, MOORE Chapter 8: Estimating with Confidence Section 8.1 Confidence Intervals: The.
©2012 Prentice Hall Business Publishing, Auditing 14/e, Arens/Elder/Beasley Audit Sampling for Tests of Controls and Substantive Tests of Transactions.
 Good for:  Knowledge level content  Evaluating student understanding of popular misconceptions  Concepts with two logical responses.
Approaches to quantifying uncertainty-related risk There are three approaches to dealing with financial and economic risk in benefit-cost analysis: = expected.
Chapter 10 Confidence Intervals for Proportions © 2010 Pearson Education 1.
The SPE Foundation through member donations
Hypothesis Testing.
Review Ordering company jackets, different men’s and women’s styles, but HR only has database of employee heights. How to divide people so only 5% of.
What’s Reasonable About a Range?
Review You run a t-test and get a result of t = 0.5. What is your conclusion? Reject the null hypothesis because t is bigger than expected by chance Reject.
Hypothesis Testing and Confidence Intervals (Part 1): Using the Standard Normal Lecture 8 Justin Kern October 10 and 12, 2017.
Session II: Reserve Ranges Who Does What
Introduction Second report for TEGoVA ‘Assessing the Accuracy of Individual Property Values Estimated by Automated Valuation Models’ Objective.
Presentation transcript:

OIL AND GAS RESERVES ESTIMATING WE HAVE MET THE ENEMY, AND HE IS US Peter R. Rose Senior Associate, Rose & Associates, LLP., and President AAPG Austin, Texas

1.Write your name on the form 2.Follow instructions 3.Try hard to answer objectively 4.Write your answers down 5.No joint ventures -- work independently 6.P90 is a small number; P10 is a large number RULES OF THE GAME

DETERMINISTIC ESTIMATE A single number (best guess) among a wide range of possible real outcomes. Low confidence in being precisely correct. Amount of uncertainty affects willingness to invest in your estimate.

FOR $20, HOW MUCH ARE YOU PREPARED TO BET THAT YOUR BEAN-ESTIMATE IS EXACTLY CORRECT? $10? $5? $2? $1? 50¢? 20¢? 10¢? 5¢? Chance of estimating the exact number of beans? !REMOTE! UNCERTAINTY & CONFIDENCE

EXPRESSING CONFIDENCE I Traditional Engineering Convention -- ±10%? Ranges of Numbers Corresponding to Confidence: Confidence (=,>) > 10 beans > 100 beans > 1,000 beans > 200 beans > 2,000 beans > 20,000 beans very, very high high moderate fairly high low very, very low Confidence (=,>)

EXPRESSING CONFIDENCE II LET US SPECIFY: “High Confidence” = 90% sure =, > P90 value “Low Confidence” = 10% sure =, > P10 value “50/50 Confidence” = 50% sure =, > P50 = Median

Superiority Of Probabilistic Method “... still just estimates cloaked in probabilities?” Advantages: 1.Measure our estimating accuracy by comparing probabilistic forecasts against actual outcomes 2.Use statistical principles to make better estimates (lognormal expectation, calculate mean of distribution, defined low-side & high-side criteria

P1Geologically possible; but extremely unlikely P10Reasonable Maximum P50Half below, Half above, the Median P90Reasonable Minimum P99As small as it could be... Yet detectable PRACTICAL ATTRIBUTES OF KEY PARAMETERS Boundary Conditions 80% confidence level

Superiority Of Probabilistic Method “... still just estimates cloaked in probabilities?” Advantages: 1.Measure our estimating accuracy by comparing probabilistic forecasts against actual outcomes 2.Use statistical principles to make better estimates (lognormal expectation, calculate mean of distribution, defined low-side & high-side criteria 3.Reality-checks -- natural limits, unrealistic extremes, analog distributions 4.Faster, more efficient

Compare your original “best estimate” (circled number, item 2, colored form) with your neighbor Does anyone in the audience have a circled number that is EXACTLY

Another bean slide... New confidence-level: NOTE: REASONABLE CERTAINTY ≠ “BEST GUESS

I am “Reasonably Certain” that there are ________ beans or more. NOTE: THIS IS A PROBABILITY STATEMENT, ALTHOUGH NO PROBABILITY (= CONFIDENCE LEVEL) IS SPECIFIED WHAT IS YOUR REASONABLE CERTAINTY? 50%? 67%? 75%? 80%? 90%? 95%? 99%? HOW CAN YOU MEASURE YOUR ESTIMATING ABILITY USING AN UNDEFINED CRITERION? 446

COMPLICATIONS 1.A larger estimate may be in your personal interest 2.Your boss may prefer larger to smaller estimates 3.Possible consequences if actual result is less than your estimate a)Negative Press b)Employer Disciplines c)State Penalizes

U.S. SEC REFUSES TO SPECIFY THE CONFIDENCE-LEVEL OF “REASONABLE CERTAINTY” 99%? 80%? 95%? 67%? 90%? 75%? 98%? 50%?

... BUT SEC will not specify confidence level assigned to “PROVED”

“Proved” Reserves...are the estimated quantities which geological and engineering data demonstrate with reasonable certainty to be recoverable....sec.gov / divisions / corpfin / forms / regsx. htm This is the foundational statement of what we think is an outdated (1978) system. Let’s take a look at the evolution of the wording reasonable certainty www

1936 Every reasonable probability 1964 Reasonable certainty 1976 Capen SPE Revised def. for proved 1987 Proved probable & possible 1978 Proved w reasonable certainty API Yergin and Hobbs, 2005 ogj SPE US SEC 1982 Year-end pricing 1997 Probabilistic methods guidelines published “Proved” Reserves reasonable certainty

DETERMINISM GETS INCREASINGLY COMPLEX Proved, Probable, Possible Developed & Undeveloped Weighting schemes to account for uncertainty ? Applicability to Undrilled Prospects and Plays?

PROBABILISTIC ESTIMATING COMES TO PETROLEUM EXPLORATION (≈ 1985  ) Exploration is a “repeated-trials game” of many uncertain ventures Statistical treatment is appropriate Statistics = “Language of Uncertainty” Aids to Improved Estimating --  Lognormality  Reality Checks  Project post-audits  improved estimating skills Leads to Portfolio Management

PROBABILISTIC ESTIMATING: STANDARD EXPLORATION PRACTICE Tipping Point mid-1990s SPE/WPC acknowledged in 1997, recognizing both Deterministic and Probabilistic approaches SPE/WPC recommendation: Proved = 90% Confidence

Reserves Estimating: A Divided Industry Exploration:Fully Probabilistic Production:Mostly Deterministic (Proved, Probable, Possible, Developed, Undeveloped, etc.)

TWO VIEWS OF E&P WORK: Deterministic View:Probabilistic View: TIME & $$ ← UNCERTAINTY → TIME & $$ ← UNCERTAINTY → “THE ANSWER” (Determinism) P90P50P10 STOP! Use this time & $$ to find other prospects

Determinism Promotes Unaccountability Attempts to represent highly uncertain parameters with a single, “precise” number, and without expressing how much uncertainty surrounds it. Proved, Probable, Possible: terms not defined quantitatively, so impossible to measure and calibrate estimating abilities objectively

So the Deterministic Method is unaccountable to: Professionals Clients and Employers Investors General Public

PROBABILISTIC METHODS PROMOTE ACCOUNTABILITY All possible outcomes are assigned likelihood of occurrence Compare estimates with outcomes: Detects and measures bias Encourages learning and improved estimating Compatible with Portfolio Management Adaptable to considerations of Chance of Success Can be universally applied to all E&P projects (Plays, Prospects, Developments, Workovers, EOR’s, etc.)

? Resistance to change? ? Propped up by SEC? ? Accountants can’t deal with uncertainty? ? Encourages false confidence? ? Desire to remain unaccountable?

DETERMINISM ENCOURAGES UNREALISTIC THINKING ABOUT HIGHLY UNCERTAIN RESOURCE VALUES Executives, Board Members, Bankers, Analysts, Stockholders Enables Decision-makers to maintain unwarranted confidence Discourages realistic assessments of uncertainty and risk

One Simple Remedy to Start Fixing the Problem A unified statement from the E&P professional community that “Proved” = 90% confidence. Imposed Definition on SEC Support of Professional Societies? Support of Influential Companies? Industry professionals created this problem -- Why don’t we, as responsible Professionals, change it? (Walt Kelly, POGO)

OIL AND GAS RESERVES ESTIMATING WE HAVE MET THE ENEMY, AND HE IS US Peter R. Rose Senior Associate, Rose & Associates, LLP., and President AAPG Austin, Texas