"A Good Science and Engineering Decision is a Good Business Decision"

 PRD ADA EPP Major Type of Competency Skill Detailed Competency Skill Description Skill Level Lecture or Exercise Hours Key Skill Skill Level Lecture or Exercise Hours Key Skill Skill Level Lecture or Exercise Hours Key Skill Administration Introductions and course administration L .50 L .50 L .50 Wrap-up L .50 L .50 L .50 1.00 1.00 1.00 Decision Analysis Process[1] Describes decision analysis[2] 2 L .75 x 2 L .75 x Identifies opportunities to apply decision analysis 3 L .25 x 3 L .25 x Follows a ten-step decision analysis process 2 L .50 x 2 L .50 x Defines key evaluation terminology (review) 2 E 1.00 x 2 E 1.00 x Selects the proper calculation method 2 L .25 x 2 L .25 x Presents a decision analysis 2 L .50 x 3 L .50 2 L .50 x Experienced with cross-disciplined teams 2 E 1.25 2 E 1.25 Understands strategies for implementing DA 2 L .50 x Recognizes common issues in implementing DA 2 L .50 x 4.50 1.50 4.50 Probability and Statistics Understands probability distributions 2 L .75 x 3 E .50 2 L .75 x Expresses or captures judgment as a distribution 2 E .50 x 2 E .50 x Produces frequency distributions 2 L .25 x 3 E .50 2 L .25 x Understands mean and standard deviation 2 E 1.00 2 E 1.00 Specifies several common distribution types 2 L .50 2 L .50 Understands methods to calculate EV[3] 2 E 1.00 x 2 E 1.00 x Knows several methods to represent correlation between variables 2 L .50 2 E 1.00 2 L .50 Draws Venn diagrams 2 E .75 2 E .75 Applies the three primary probability rules 3 E 1.50 x 3 E 1.50 x Constructs and uses joint probability tables 3 E .75 3 E .75 Applies Bayes' rule in revising probabilities 2 E 3.00 x 3 E .50 1 L .50 10.50 2.50 8.00 Decision Policy Describes three components of decision policy 2 L 1.50 x 2 L 1.50 x Can apply multi-criteria decision making 1 L .25 2 L .50 x 1 L .50 Understands ways to represent HSE values 2 L .25 2 L .50 x 2 L .25 Can work with utility function for risk policy 2 E 5.00 x 2.00 6.00 2.25 Modeling Describes problem with influence diagram 1 L .50 2 E 1.00 x 2 E 1.00 Performs the PV calculation 1 E .50 x 2 E .75 x 2 E 1.50 x Performs escalation and inflation calculations 1 L .25 2 E .50 x 3 E 1.00 x Familiar with several sensitivity analysis techniques 1 L .25 x 2 E .25 x 2 L .25 x Understands optimization, including bidding 1 L .50 2 E[4] 1.00 x 1 L .50 Describes probabilistic reserves issues 1 L .50 2 E 1.00[5] x Familiar with volumetric method and exponential decline 2 E .50 x 2 L .50 x Can model a multi-pay drilling location 2 E 2.00 x 2 E 2.00 x 1 E 2.00[6] Outlines how to mode plays and basins 1 L .25 2 L 1.00 x 1 L .25 Aware of discovery process and resource assessment models 1 L .75 Follows good Excel modeling techniques 1 L .25 2 E .50 x 3 L 1.00 x Describes modeling tricks and traps 2 L .75 x 2 L .75 x Identifies project schedule modeling concepts[7] 2 E 1.00 x Identifies some emerging evaluation technologies[8] 1 L 1.50 x 5.00 11.50 9.75 Economics, Accounting and Finance Understands the PV discount rate 2 L .50 3 L 1.00 2 L 1.00 Knows how to optimize a portfolio under capital constraint 2 L .50 2 L .50 x 2 L .50 Aware of basic portfolio management concepts 1 L .50 2 L 1.00 x 1 L .50 Understands the concept of options 2 L 1.00 x Knows some basic accounting concepts 1 L 1.00 1 L .75 1.50 4.50 2.75 Decision Tree Analysis Can model a problem as a decision tree 2 E 3.75 x 2 E 2.00 x 2 E 3.75 x Can value imperfect information 2 E 3.00 x 3 E 1.00 x 1 L .75 Can value imperfect control 2 E 2.00 x 2 E 2.00 x Has experienced decision tree software 1 L .50 2 E 2.00 x 1 L .50 Can calculate expected utility and certain equivalent 3 E 1.00 x 9.25 6.00 7.00 Monte Carlo Simulation Understands the priciples of MC simulation 2 L 1.00 x 2 L 1.00 x Understands Latin hypercube sampling 2 L .50 x 2 L .50 x Can solve for EV using MCS 2 E 1.00 x 2 E 1.00 x Understands stochastic variance causes and effect 2 L .50 x 3 E .75 x 2 L .50 x Experience with Monte Carlo simulation software 1 L[9] .50 1 E 1.50 x 1 L[10] .50 Understands several approaches for a Monte Carlo stopping rule 2 L 1.00 x Can calculate expected utility and certain equivalent 3 E .50 x 3.50 23 x's 3.75 28 x's 3.50 25 Topics if Time Permit or of Special Interest for In-House Courses Competitive bidding simulation Project risk management Red fields in EPP indicates where Additional practice/company examples emphasis or topic is different from PRD. Psychological biases Subtotals 37.25 36.75 38.75 PRD = Petroleum Risks and Decision Analysis Checksum 37.25 36.75 38.75 ADA = Applied Decision Analysis with Portfolio and Project Modeling EPP = Economic Evaluation of Prospects and Producing Properties Lecture 14.25 13.00 17.75 Exercises[11] 23.00 23.75[12] 21.00[13] 37.25 36.75 38.75

[1]
These are notebook tab headings; plus Major Exercises, Appendices, Index
[2]
Suitable as a standalone briefing presentation.
[3]
demo CLT via MCS
[4]
the optimization is the life insurance problem
[5]
part of Probabilistic Reserves Simulation
[6]
part of Probabilistic Reserves Simulation
[7]
incl CAPM and PERT
[8]
e.g., fuzzy logic, neural networks, expert systems and genetic algorithms.
[9]
demo
[10]
demo
[11]
including  some lecture
[12]
including  some lecture
[13]
including  some lecture