Upstream ModuleEAM/UPM

Who should attend?

  • To gain an understanding of the upstream petroleum sector in its technical, economic and financial dimensions (main technical mechanisms, key economic data and characteristics, management tools, etc.).
Audience :
  • Recently hired professionals, preferably with an engineering background, about to take up a position in upstream petroleum activities.
  • Staff from other petroleum sectors (refining, chemicals, etc.) taking up an upstream managerial position or from government agencies with responsibilities for petroleum matters will also benefit from this course.

Level : Proficiency

Course Content

  • PRODUCTION & RESERVOIR ENGINEERING

      • This part of the course covers the basic techniques used in exploration, development and production. This will enable the participants to communicate with specialists in this field, understand and estimate the validity of the technical data on which economic analyses are based.
  • GLOBAL ENERGY OUTLOOK

      • Energy geopolitics.
      • Energy issues in the context of policy.
      • Financial aspects of the petroleum industry.
      • Evolution of the oil industry.
  • UPSTREAM MANAGEMENT

      • Economic aspects of Oil & Gas Exploration-Production.
      • Legal and fiscal aspects.
      • Project financing.
  • EVALUATION OF PROJECTS

      • Economic criteria: discounted cash flow, Internal Rate of Return, profitability index.
      • Field development case study.
      • Equivalent cost and long-term marginal cost.
      • Portfolio management.
  • FINANCE & ACCOUNTING

      • Principles of accounting: case of Oil & Gas companies.
      • The balance sheet and the income statement and notes.
      • Financial analysis.
      • Market value, Price Earnings Ratio.
      • Statement of cash flow.
      • Introduction to cost accounting and management control.
  • EFFICIENCY ANALYSIS OF INDUSTRIAL FIRMS

      • Production frontier and economic performances of firms.
      • Deterministic, stochastic parametric and non-parametric models.

Ways & Means

  • Case studies.
  • Statistical data.