Petroleum Risk and Decision Analysis - PRD - In House Training

Course

In San Francisco (USA)

Price on request

Description

  • Type

    Course

  • Location

    San francisco (USA)

  • Start date

    Different dates available

Petroleum Risk and Decision Analysis - In-house training by PetroSkillsThis course can be conducted on site at your location exclusively for your staff. Good technical and business decisions are based on competent analysis of project costs, benefits and risks. Participants learn the decision analysis process and foundation concepts so they can actively participate in multi-discipline evaluation teams. The focus is on designing and solving decision models. Probability distributions express professional judgments about risks and uncertainties and are carried through the calculations. Decision tree and influence diagrams provide clear communications and the basis for valuing each alternative. The complementary Monte Carlo simulation technique is experienced in detail in a hand-calculation exercise. Project modeling fundamentals and basic probability concepts provide the foundation for the calculations. The mathematics is straightforward and mostly involves only common algebra. The emphasis is on practical techniques for immediate application. This is a fast-paced course and recommended for those with strong English listening skills. This course is intended as the prerequisite for the Advanced Decision Analysis with Portfolio and Project Modeling course. Find out more

Facilities

Location

Start date

San Francisco (USA)
See map
333 Bush Street, Suite 2400, 94104

Start date

Different dates availableEnrolment now open

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Subjects

  • Project
  • Risk
  • IT risk
  • Probability
  • Petroleum
  • Basic IT training
  • Simulation
  • Basic
  • Basic IT

Course programme

Training Course Content

This in-house training programme can be customised to meet your specific requirements. It typically covers:

  • Decision Tree Analysis: decision models; value of information (a key problem type emphasized in the course), flexibility and control; project threats and opportunities
  • Monte Carlo Simulation: Latin hypercube sampling; portfolio problems; optimization; advantages and limitations
  • Decision Criteria and Policy: value measures; multiple objectives; HSE; capital constraint; risk aversion
  • Modeling the Decision: influence diagrams; sensitivity analysis; modeling correlations
  • Basic Probability and Statistics: four fundamental rules, including Bayes' rule; calibration and eliciting judgments; choosing distribution types; common misconceptions about probability
  • Expected Value Concept: foundation for decision policy, features, pitfalls to avoid
  • Implementing Decision Analysis: problem framing; guidelines for good analysis practice; team analyses; computer tools (discussion and demonstrations); mitigating risks
  • Evaluating a multi-pay prospect (team exercise).

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Additional information