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Develop measurable skills and capabilities
Risks and uncertainties are everywhere in E&P projects. Theyimpact decision making by the projects chosen, how they aredeveloped, and their economic performance. Improving the quality of decisionsis the main goal, not just understanding risk and uncertainty for their ownsake.
Probabilistic concepts and tools are generally used to modelrisk and uncertainty, and sometimes misused due to absent or long forgottenstatistical training. The purpose of this course is to give you a soundunderstanding of the most important statistical concepts used in E&Pprojects, and apply them in hands-on case studies.
The instructor, Dr. Pierre Delfiner is the recipient of the2018 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 ofInformation (VOI) by combining decision trees and Bayesian inversion to show howthe probability of geologic success is updated to incorporate new seismicinformation, 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 thedifference?
· 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 dealwith themThe flaw of averages, the winner’s curse
Monte Carlo and Applications
· A review of the most common distributions usedin geosciences
· The Monte Carlo approach
· Presentation of @RISK™, a Monte Carlo add-in forExcel™
· Computing uncertainty on STOIIP
· Workover case study
Monte Carlo simulation is an essential tool to generateoutcomes in the presence of risk and uncertainty. Examples explain how todevelop a Monte Carlo model and interpret the results. The importance of scalein assessing uncertainty is illustrated by a workover program design casestudy.
Cluster development case study and portfolio issues
· Shared risks model in exploration
· Cluster development evaluations under geologicaldependencies
· The PRMS resources classification system
· Efficient Frontier
· Why diversify assets?
The case study is about the development of geologicallydependent 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 frompressure data
Correlation and regression are handy to represent therelationship between two variables, but may give nonsensical results if used ina push-button way without understanding of the underlying assumptions. A casestudy involving pressure data and fluid density shows how to establish aregression model.
The course ends with a wrap-up of the week, main technicalmessages, 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.