Risk, Uncertainty and Decisions in E&P Projects - Separate Agenda

Risk

• Probability definitions, objective and subjective probabilities, calculation rules, expected value
• Project economics under risk
• Decision Analysis, decision criteria
• Conditional probabilities and Bayes formula
• Decision trees
• Case study: updating probabilities of success with new seismic information

The course will start with a definition of risk and uncertainty and examples in E&P projects. After a review of the essentials of probability and a reminder of basic economic concepts (cash flow, discounting, NPV, EMV), a simple decision model will illustrate the relationship between risk, cost, and benefit. Decision Analysis and decision trees will be presented and a practical case study will show how new seismic information impacts the probability of success.

Value of information  

• Case study continued with Value of Information (VOI)
• VOI of a 4D seismic survey to decide and locate an injector well
• VOI to compare alternative acquisition solutions
• Wrap-up on VOI, caveats and messages
• The psychology of decision making

The case study continues with the assessment of the economic value of the seismic information, and how its quality impacts its value. Two additional case studies involving 4D seismic will be discussed, one to include or not the shooting of a 4D survey as a production monitoring tool to locate an injector well, and another to compare a permanent monitoring system (LoFS) with alternative acquisition solutions. The day ends with a discussion of the psychological aspects of decision making, utility functions, risk attitudes, and cognitive biases

Uncertainty  

• Deterministic versus probabilistic: what is the difference?
• Describing uncertainty: population, samples, histogram, pdf, cdf, percentiles
• Mean, mode, median; variance, standard deviation, coefficient of variation
• Standard error of the mean
• Random and systematic errors and how to deal with them
• The effects of diversification

The winner’s curse in competitive bidding The main tools to characterize uncertainty will be presented in Day 3: descriptive statistics, probability distributions commonly used in geosciences (normal, lognormal, triangular, uniform, stretched beta, Bernoulli, binomial), and confidence intervals. Estimation accuracy, the different types of errors and how to reduce them will be reviewed. Finally, an exercise will illustrate the effects of diversification, and the day will end with a discussion of the winner’s curse. 

Monte Carlo and Applications

• The Monte Carlo approach
• Presentation of @RISK™, a Monte Carlo add-in for Excel™
• Computing uncertainty on STOIIP
• Workovers case study
• Modeling dependencies between risks 

Day 4 will be devoted to Monte Carlo simulation and hands-on case studies. A practice problem on estimation of the production increase expected from a workover program will reveal the importance of scale in assessing uncertainty. A comprehensive project case study with multiple dependent objects will have the participants model shared geological risks, build the probability tree of the different outcomes, derive the distribution of aggregate resources, and select development scenarios for planning and economics

Correlation and regression

• The correlation coefficient, its interpretation, and its effect on uncertainty
• Simpson’s paradox and confounding factors
• Least squares
• The predictive value of a trend line
• Case study: estimation of fluid density from pressure data

Day 5 will focus on the widely used, and often misused, statistical techniques of correlation and regression.  Does correlation imply causation? What is its effect on uncertainty? How to quality check a Least Squares fit? Uncertainty on fluid densities computed from MDT pressure data will take participants through the various steps involved in establishing and checking a regression model and deriving prediction uncertainty.