Petroleum Engineering Decision-Making Under Uncertainty Training Course

Petroleum Engineering Decision-Making Under Uncertainty Training Course

Introduction

Petroleum Engineering Decision-Making Under Uncertainty Training Course is designed to equip oil and gas professionals with advanced analytical skills, quantitative decision-making techniques, and risk management strategies for making reliable engineering and investment decisions in complex petroleum environments. The course explores how subsurface uncertainty, reservoir heterogeneity, geological risk, production variability, fluctuating oil prices, and operational constraints influence petroleum project outcomes. Participants will learn to apply probability theory, statistical analysis, sensitivity analysis, scenario planning, Monte Carlo simulation, Bayesian decision analysis, and decision tree modeling to evaluate competing development options and optimize technical and commercial performance. Through practical applications in reservoir engineering, drilling operations, field development planning, production optimization, and capital investment evaluation, learners will develop the ability to translate incomplete or uncertain data into actionable engineering insights.

The course also emphasizes data-driven petroleum engineering, probabilistic forecasting, uncertainty quantification, value of information (VoI), real options analysis, and risk-informed asset management to strengthen strategic decisions throughout the petroleum asset lifecycle. Participants will explore how to integrate geological, geophysical, reservoir simulation, economic, and operational data into robust decision frameworks that support effective resource allocation and improved project resilience. Industry-based case studies will demonstrate how petroleum organizations can compare alternative development scenarios, assess downside risks, quantify economic exposure, and identify opportunities to maximize expected value under uncertainty. By the end of the training, participants will be better prepared to make defensible engineering recommendations, communicate uncertainty to stakeholders, improve investment decisions, and support sustainable value creation across upstream oil and gas projects.

Course Duration

5 days

Course Objectives

By the end of this course, participants will be able to:

1. Understand uncertainty quantification and its implications for petroleum engineering decisions.
2. Apply probability theory and statistical modeling to evaluate petroleum engineering risks.
3. Develop probabilistic reservoir models to assess subsurface uncertainty and resource potential.
4. Use Monte Carlo simulation to estimate production forecasts, project costs, and economic outcomes.
5. Perform sensitivity analysis and scenario modeling to identify critical project success factors.
6. Apply decision tree analysis to compare alternative drilling and field development strategies.
7. Integrate Bayesian updating and new data assimilation into evolving engineering decisions.
8. Evaluate value of information (VoI) to determine the economic benefit of additional data acquisition.
9. Implement risk-based decision analysis for exploration, appraisal, drilling, and production operations.
10. Assess uncertainty in reserves estimation and production forecasting using probabilistic methods.
11. Apply real options analysis to evaluate investment flexibility under changing market conditions.
12. Strengthen data-driven decision-making and uncertainty communication for multidisciplinary teams.
13. Develop robust petroleum project strategies that balance technical risk, economic value, and operational performance.

Target Audience

1. Petroleum engineers and senior petroleum engineering professionals.
2. Reservoir engineers and reservoir simulation specialists.
3. Drilling engineers and well planning professionals.
4. Geologists, geophysicists, and subsurface interpretation specialists.
5. Production engineers and field development planners.
6. Petroleum economists, project finance analysts, and investment evaluators.
7. Risk managers, asset managers, and upstream project managers.
8. Data scientists, technical consultants, and decision analysts working in the oil and gas industry.

Course Modules

Module 1: Fundamentals of Decision-Making Under Uncertainty

• Principles of uncertainty, variability, and incomplete information in petroleum engineering.
• Types of uncertainty: geological, technical, operational, economic, and regulatory.
• Distinguishing aleatory uncertainty from epistemic uncertainty.
• Introduction to expected value, utility theory, and decision criteria.
• Case study: Evaluating whether to proceed with an exploration well when seismic interpretation and hydrocarbon presence remain uncertain.

Module 2: Probability Theory and Statistical Analysis for Petroleum Engineering

• Probability distributions and their applications in petroleum engineering.
• Descriptive statistics, correlation analysis, and uncertainty characterization.
• Normal, lognormal, triangular, uniform, and beta distributions.
• Probabilistic estimation of porosity, permeability, reservoir volume, and recovery factors.
• Case study: Using probability distributions to estimate uncertainty in original oil in place (OOIP) for a heterogeneous reservoir.

Module 3: Reservoir Uncertainty and Probabilistic Resource Estimation

• Sources of geological and geophysical uncertainty in reservoir characterization.
• Uncertainty propagation in volumetric reserves estimation.
• Probabilistic reserves and resources assessment using P90, P50, and P10 estimates.
• Integration of seismic interpretation, well logs, core data, and pressure measurements.
• Case study: Comparing low-, mid-, and high-case recoverable resource estimates to determine the commercial potential of an offshore discovery.

Module 4: Monte Carlo Simulation and Quantitative Risk Analysis

• Fundamentals of Monte Carlo simulation for petroleum project evaluation.
• Defining input variables, probability distributions, and model dependencies.
• Simulating uncertainty in reserves, production rates, costs, and oil prices.
• Interpreting cumulative probability curves and probabilistic forecasts.
• Case study: Simulating project economics for a new field development to estimate the probability of achieving a target net present value (NPV).

Module 5: Sensitivity Analysis, Scenario Planning, and Robust Decisions

• One-way and multi-variable sensitivity analysis.
• Tornado diagrams, scenario matrices, and critical parameter identification.
• Best-case, base-case, and worst-case petroleum development scenarios.
• Robust decision-making under changing technical and commercial assumptions.
• Case study: Determining how recovery factor, drilling costs, oil prices, and production decline influence the profitability of a marginal oil field.

Module 6: Decision Trees, Bayesian Analysis, and Value of Information

• Constructing decision trees for exploration, appraisal, and development decisions.
• Calculating expected monetary value (EMV) and comparing alternative strategies.
• Bayesian updating of geological and reservoir probabilities as new evidence emerges.
• Evaluating the value of seismic surveys, appraisal wells, and production testing.
• Case study: Comparing immediate field development with an appraisal drilling strategy that could reduce uncertainty before major capital investment.

Module 7: Economic Risk, Investment Optimization, and Real Options

• Risk-adjusted cash flow analysis and probabilistic NPV evaluation.
• Oil price volatility, capital expenditure uncertainty, and operating cost exposure.
• Real options for staged development, project expansion, delay, and abandonment.
• Decision-making under fiscal, regulatory, and market uncertainty.
• Case study: Evaluating whether an operator should develop a marginal offshore field immediately or defer investment until improved market conditions and additional reservoir data become available.

Module 8: Integrated Decision Frameworks and Petroleum Industry Applications

• Integrating subsurface, engineering, operational, and economic uncertainty.
• Multi-criteria decision analysis (MCDA) for comparing development alternatives.
• Risk registers, decision quality frameworks, and uncertainty communication.
• Building decision dashboards and evidence-based engineering recommendations.
• Case study: Selecting an optimal field development plan by balancing recovery potential, production reliability, environmental exposure, capital cost, and project uncertainty.

Training Methodology

• Interactive lectures and presentations.
• Group discussions and brainstorming sessions.
• Hands-on exercises using real-world datasets.
• Role-playing and scenario-based simulations.
• Analysis of case studies to bridge theory and practice.
• Peer-to-peer learning and networking.
• Expert-led Q&A sessions.
• Continuous feedback and personalized guidance.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104 

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

 We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.