Pideya Learning Academy

Petroleum Economics and Risk Analysis Techniques

Upcoming Schedules

  • Schedule

Date Venue Duration Fee (USD)
13 Jan - 17 Jan 2025 Live Online 5 Day 2750
31 Mar - 04 Apr 2025 Live Online 5 Day 2750
28 Apr - 02 May 2025 Live Online 5 Day 2750
19 May - 23 May 2025 Live Online 5 Day 2750
11 Aug - 15 Aug 2025 Live Online 5 Day 2750
22 Sep - 26 Sep 2025 Live Online 5 Day 2750
17 Nov - 21 Nov 2025 Live Online 5 Day 2750
08 Dec - 12 Dec 2025 Live Online 5 Day 2750

Course Overview

The global energy landscape is undergoing a fundamental transformation, driven by shifting demand, geopolitical volatility, price fluctuations, and the accelerating transition to low-carbon alternatives. In such a dynamic environment, the ability to understand, quantify, and mitigate economic risks in petroleum projects is no longer optional—it is essential. The Petroleum Economics and Risk Analysis Techniques training by Pideya Learning Academy is designed to help industry professionals master the principles of petroleum project valuation and economic modeling while developing a risk-sensitive mindset that supports sound decision-making across the project lifecycle.
Today’s oil and gas professionals must evaluate capital-intensive projects that involve considerable uncertainty in reserves estimation, production performance, market pricing, and regulatory shifts. According to a 2023 report by the International Energy Agency (IEA), over $600 billion in upstream investments are expected globally by 2030, highlighting the critical role of economic analysis in portfolio management, cost control, and sustainability. Additionally, research from Rystad Energy shows that nearly 70% of oil and gas projects experience cost overruns due to inadequate risk forecasting and mitigation strategies. These figures underscore the necessity for robust financial frameworks and analytical tools in petroleum economics.
This course offers a comprehensive exploration of economic evaluation techniques, project cash flow modeling, fiscal regimes, cost recovery, profitability indicators, and probabilistic risk analysis. The curriculum is tailored to real-world decision contexts where professionals must balance financial return expectations with technical and operational constraints. The training will also emphasize how macroeconomic trends, oil price volatility, and environmental pressures shape project feasibility and investment priorities in both mature and frontier markets.
Participants will learn to apply economic metrics such as Net Present Value (NPV), Internal Rate of Return (IRR), and Payback Period while exploring deterministic and probabilistic approaches to sensitivity and scenario analysis. Emphasis is placed on upstream oil and gas investments, including field development planning, exploration asset appraisal, and production asset valuation. Global case studies and fiscal system comparisons will provide contextual depth and actionable insights.
Key highlights of this Pideya Learning Academy training include:
Advanced techniques for modeling project cash flows, CAPEX/OPEX, and revenue forecasts
Evaluation of global petroleum fiscal regimes and their impact on project returns
Methods for integrating technical, commercial, and geopolitical risks into economic models
Use of decision trees, Monte Carlo simulations, and sensitivity analysis to quantify uncertainty
Analysis of breakeven prices and project viability under fluctuating market conditions
Exposure to real-life economic assessment scenarios and lessons from industry failures
Strategic insight into how petroleum economics supports asset acquisition and divestment decisions
Through this training, professionals will develop the capability to communicate economic risks and rewards effectively to internal and external stakeholders—including investors, government agencies, and board-level executives. This skillset is especially vital in today’s energy sector, where project sponsors are expected to demonstrate both financial accountability and long-term sustainability.
Whether preparing investment proposals, participating in field development planning, or engaging in exploration strategy, course participants will walk away with a solid grounding in petroleum economics principles and tools, empowering them to make informed and resilient business decisions. By bridging theory with strategy, Pideya Learning Academy ensures that learners acquire not only technical competence but also the economic foresight to thrive in a complex and competitive energy market.

Key Takeaways:

  • Advanced techniques for modeling project cash flows, CAPEX/OPEX, and revenue forecasts
  • Evaluation of global petroleum fiscal regimes and their impact on project returns
  • Methods for integrating technical, commercial, and geopolitical risks into economic models
  • Use of decision trees, Monte Carlo simulations, and sensitivity analysis to quantify uncertainty
  • Analysis of breakeven prices and project viability under fluctuating market conditions
  • Exposure to real-life economic assessment scenarios and lessons from industry failures
  • Strategic insight into how petroleum economics supports asset acquisition and divestment decisions
  • Advanced techniques for modeling project cash flows, CAPEX/OPEX, and revenue forecasts
  • Evaluation of global petroleum fiscal regimes and their impact on project returns
  • Methods for integrating technical, commercial, and geopolitical risks into economic models
  • Use of decision trees, Monte Carlo simulations, and sensitivity analysis to quantify uncertainty
  • Analysis of breakeven prices and project viability under fluctuating market conditions
  • Exposure to real-life economic assessment scenarios and lessons from industry failures
  • Strategic insight into how petroleum economics supports asset acquisition and divestment decisions

Course Objectives

After completing this Pideya Learning Academy training, the participants will learn to:
Understand the fundamental processes and purposes of Special Core Analysis
Identify the influence of drilling fluids and coring techniques on sample integrity
Evaluate laboratory procedures and recognize their limitations and biases
Detect erroneous SCAL data and apply appropriate refinement methods
Integrate SCAL data with reservoir simulation and classical engineering calculations
Analyze the economic impact of SCAL data on reservoir management decisions
Optimize laboratory testing strategies for improved data quality and reliability
Collaborate effectively with multidisciplinary teams in SCAL-driven reservoir studies

Personal Benefits

Participants attending this course will gain:
In-depth knowledge of core analysis methods and best practices
Enhanced capabilities in identifying and rectifying flawed data
Improved confidence in applying SCAL data to real-world scenarios
Broader understanding of global benchmarks and field applications
Greater professional value within multidisciplinary reservoir teams

Organisational Benefits

Organizations enrolling their employees in this training program will benefit from:
Improved accuracy in reservoir modeling and forecasting
Enhanced cross-functional collaboration among technical teams
Reduced risk of investment errors due to misinterpreted SCAL data
More efficient and cost-effective laboratory testing processes
Strengthened technical decision-making in reservoir management

Who Should Attend

This course is ideal for:
Reservoir Engineers involved in simulation or analytical reservoir evaluations
Supervisors and managers overseeing subsurface engineering projects
Laboratory professionals engaged in core sample testing and reporting
Production Geologists working on reservoir characterization
Petrophysicists contributing to integrated reservoir studies
Technical personnel seeking to improve their understanding of SCAL data integration

Course Outline

Module 1: Fundamentals of Energy Project Valuation
Evaluation methodologies in oil and gas projects Overview of investment decision-making criteria Stages in the upstream oil and gas project lifecycle Stakeholders and functional roles across the project timeline Classification of upstream development projects Production estimation techniques Price forecasting models in hydrocarbon markets Operating cost modeling and trend analysis Introduction to economic screening techniques Cost escalation and inflation considerations in project planning
Module 2: Constructing Financial Models for Energy Projects
Components of cash flow statements in oil & gas operations Revenue estimation under variable pricing structures Fixed vs. variable operating expenditure (OPEX) Capital expenditure (CAPEX) planning and allocation Net cash flow projections and temporal cash flow curves Time value of money and discounting methodologies Present value and future value in financial modeling Introduction to financial feasibility and investment thresholds Role of economic assumptions in project analysis Non-economic value drivers (strategic value, compliance, etc.)
Module 3: Economic Performance Metrics in Oil & Gas
Net Present Value (NPV) calculation methods Internal Rate of Return (IRR) and interpretation Discounted Payback Period and profitability index Modified Internal Rate of Return (MIRR) Weighted Average Cost of Capital (WACC) application Cost of equity and debt in oil and gas sector Hurdle rate determination for energy investments Decision criteria based on capital rationing Sensitivity of metrics to project variables Comparative assessment of economic indicators
Module 4: Petroleum Fiscal Systems and Investment Structures
Overview of global fiscal regimes in the petroleum sector Structural elements of concessionary systems Production Sharing Contracts (PSCs) and sliding scales Risk Service Contracts and Technical Service Agreements Government take and investor share modeling Taxation, royalties, and bonus payments Cash flow modeling under various fiscal arrangements Fiscal stability clauses and renegotiation provisions Economic efficiency of fiscal regimes Emerging trends in host country contract design
Module 5: Risk-Informed Project Decision Analysis
Definition and classification of project risks Deterministic vs. probabilistic project evaluations Sensitivity analysis on key variables (price, cost, production) Expected Monetary Value (EMV) and risk quantification Expected Net Present Value (ENPV) techniques Scenario planning and Monte Carlo simulation Real options in upstream investment analysis Portfolio optimization and multi-project comparison Incremental investment decision-making Ranking and prioritization under uncertainty
Module 6: Capital Allocation and Budgeting Strategies
Corporate capital allocation frameworks Strategic fit and non-financial project appraisal criteria Investment planning cycle in upstream portfolios Internal competition for capital and project screening Post-audit of capital projects and lessons learned Integrating ESG (Environmental, Social, Governance) into investment decisions Cross-functional coordination in budgeting and approvals Governance and assurance in investment appraisal Role of decision gates and stage-gate processes Long-term planning versus short-term investment trade-offs
Module 7: Technical Data Interpretation for Investment Decisions
Evaluation of water-oil relative permeability datasets Assessment of gas-oil relative permeability data Integration of laboratory data into reservoir models Primary drainage capillary pressure curve analysis Quality control of rock-fluid interaction data Use of petrophysical data in economic evaluations Impact of reservoir uncertainty on production forecasts Reservoir simulation input from experimental data Techniques for refining relative permeability curves Case studies in production estimation refinement
Module 8: Integrated Forecasting and Uncertainty Management
Combining economic, technical, and fiscal inputs in forecasting Volatility modeling in oil and gas markets Reserves uncertainty and probabilistic forecasting Use of historical analogs and regression analysis Integrated economic-reservoir forecast models Price decks and their influence on investment decisions Risk-adjusted production forecasting Stress-testing financial models Dynamic modeling of CAPEX and OPEX escalations Integration of stochastic modeling tools

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