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Risks and uncertainties are everywhere in E&P projects. They impact decision making by the projects chosen, how they are developed, and their economic performance. Improving the quality of decisions is the main goal, not just understanding risk and uncertainty for their own sake.
Probabilistic concepts and tools are generally used to model risk and uncertainty, and sometimes misused due to absent or long forgotten statistical training. The purpose of this course is to give you a sound understanding of the most important statistical concepts used in E&P projects, and apply them in hands-on case studies.
The instructor, Dr. Pierre Delfiner is the recipient of the 2018 Society of Petroleum Engineers’ International Award for Management & Information.
Risk and Decision Analysis
· Probability, objective and subjective, calculation rules, expected value
· Probability calculation rules
· Expected value, risk dependence
· Simple exploration economics exercise
· Decision trees
· Influence diagrams, SWOT analysis
· Bayes formula
· Value of Information (VOI) case study
The case study exemplifies the concept of Value of Information (VOI) by combining decision trees and Bayesian inversion to show how the probability of geologic success is updated to incorporate new seismic information, and how the quality of this information impacts its value.
Decision continued and Uncertainty
Psychological Aspects of Decision Making
· Utility curves and risk attitudes
· Behavioral economics
· How good are we at estimating?
· Cognitive biases
· Deterministic versus probabilistic: what is the difference?
· Histogram, pdf, cdf, percentiles, mean, mode, median, variance, standard deviation, risked mean
· Standard error of the mean
· Confidence intervals
· Random and systematic errors and how to deal with themThe flaw of averages, the winner’s curse
Monte Carlo and Applications
· A review of the most common distributions used in geosciences
· The Monte Carlo approach
· Presentation of @RISK™, a Monte Carlo add-in for Excel™
· Computing uncertainty on STOIIP
· Workover case study
Monte Carlo simulation is an essential tool to generate outcomes in the presence of risk and uncertainty. Examples explain how to develop a Monte Carlo model and interpret the results. The importance of scale in assessing uncertainty is illustrated by a workover program design case study.
Cluster development case study and portfolio issues
· Shared risks model in exploration
· Cluster development evaluations under geological dependencies
· The PRMS resources classification system
· Efficient Frontier
· Why diversify assets?
The case study is about the development of geologically dependent reservoirs drilled by a single well and of a cluster of dependent, geographically dispersed, fields drilled separately.
Correlation and Regression
· Pearson and Spearman correlation coefficients
· Correlation and causation
· Simpson’s paradox and confounding factors
· Effect of correlation on uncertainty
· Fitting equations to data by Least Squares
· The predictive value of a trend line
· Case study: estimating fluid density from pressure data
Correlation and regression are handy to represent the relationship between two variables, but may give nonsensical results if used in a push-button way without understanding of the underlying assumptions. A case study involving pressure data and fluid density shows how to establish a regression model.
The course ends with a wrap-up of the week, main technical messages, and Q&A from the audience.
Anyone interested in learning more about the approach and techniques of risk, uncertainty, and decisions in E&P projects.
Some basic statistics knowledge would be a plus but is not required.
Customize your own learning journey and track your progress when you start using a defined learning path.