AI for Upstream Oil & Gas: Reservoir Modelling and Production Forecasting

A Professional Training Course On:

AI for Upstream Oil & Gas: Reservoir Modelling and Production Forecasting

Leveraging Advanced Machine Learning and Subsurface Data Analytics to Optimise Asset Performance and Strategic Field Development

Course Schedule

About This AI for Upstream Oil & Gas: Reservoir Modelling and Production Forecasting Training Course

Subsurface uncertainty, evolving dynamic operating conditions, and complex subsurface physics present continuous challenges to asset management and operational decision-making across the energy sector. Modern reservoir engineering relies on the continuous evaluation of extensive geological, petrophysical, and dynamic production data to refine field development plans, optimize well placement strategies, and ensure accurate reserves estimation. Integrating advanced machine learning framework into conventional analytical routines enhances subsurface evaluation, enables rapid scenario testing, and improves predictive accuracy across the entire lifecycle of an oil and gas asset.

AI for Upstream Oil & Gas: Reservoir Modelling and Production Forecasting training course provides subsurface professionals with the practical methodologies required to augment conventional engineering models with data-driven predictive techniques. By establishing robust workflows that connect data preparation, feature engineering, and model validation with physical domain constraints, this training course equips teams to evaluate risk, communicate uncertainty, and optimize critical investment decisions.

Expected Outcomes

Completing the AI for Upstream Oil & Gas: Reservoir Modelling and Production Forecasting training course equips subsurface specialists and technical leaders with the following practical capabilities:

  • Integrate complex static geological data with dynamic production measurements to build robust subsurface predictive workflows.
  • Identify key operational, rock, and fluid parameters to enhance feature engineering and model accuracy.
  • Evaluate the comparative performance of conventional decline curve techniques against advanced machine learning algorithms.
  • Implement effective validation protocols to prevent overfitting, data leakage, and misleading forecast outputs.
  • Quantify subsurface and operational uncertainties to construct reliable, risk-adjusted production forecast scenarios.
  • Incorporate physical reservoir laws and material balance constraints into data-driven models.
  • Formulate structured implementation strategies to deploy predictive analytics across upstream operations.

This Course is Best For

The AI for Upstream Oil & Gas: Reservoir Modelling and Production Forecasting training course is specifically designed for technical professionals and managers seeking to enhance subsurface performance through advanced analytics, including:

  • Reservoir Engineers
  • Petroleum Engineers
  • Production Engineers
  • Well Performance Specialists
  • Geoscientists
  • Petrophysicists
  • Field Development Planners
  • Asset Optimization Engineers
  • Upstream Data Scientists
  • Subsurface Data Analysts
  • Technical Asset Managers

Training Method

This training course utilizes a practical, highly interactive learning approach centered on technical presentations, structured group discussions, and guided analytical exercises. Participants engage directly with realistic upstream datasets to explore data pre-processing, model calibration, and comparative forecast evaluation. Collaborative exercises encourage peer-to-peer knowledge sharing and professional reflection, allowing technical teams to thoroughly examine model limitations, data assumptions, and practical decision-making strategies.

Under the guidance of experienced domain specialists, participants gain hands-on experience in balancing empirical model outputs with fundamental physical principles. The methodology focuses on developing critical engineering interpretation skills, ensuring that participant insights translate into actionable operational strategies and enhanced decision support for complex field operations.

Course Outline

Day 1:Upstream Data and AI Applications
  • Reservoir modelling and production forecasting decisions across the asset lifecycle
  • Conventional reservoir engineering and forecasting approaches
  • AI and machine learning applications in upstream operations
  • Sources of geological, petrophysical, well and production data
  • Data quality, missing values and inconsistent reporting
  • Aligning production data with well events and operating conditions
  • Defining the forecast target, time horizon and decision context
  • Selecting an upstream use case for analysis
Day 2:Preparing Data and Characterising Reservoir Behaviour
  • Integrating static reservoir and dynamic production data
  • Selecting features related to rock, fluid and well performance
  • Analysing pressure, rates, water cut and gas–oil ratio trends
  • Accounting for shut-ins, workovers and artificial lift changes
  • Identifying outliers and separating errors from significant events
  • Segmenting wells and reservoirs with comparable characteristics
  • Exploring relationships between inputs and production outcomes
  • Documenting data assumptions and limitations
Day 3:AI Methods for Reservoir Modelling
  • Using AI to support reservoir characterisation
  • Predicting reservoir properties from available measurements
  • Identifying patterns across wells and geological zones
  • Developing proxy models for rapid scenario evaluation
  • Comparing AI predictions with geological and engineering understanding
  • Incorporating physical constraints into model evaluation
  • Validating results where subsurface observations are limited
  • Interpreting model outputs for field development decisions
Day 4:AI-Based Production Forecasting
  • Establishing decline curve and engineering forecast benchmarks
  • Preparing time-series data for well and field forecasts
  • Comparing machine learning approaches for production prediction
  • Defining training, validation and test periods
  • Preventing data leakage and unrealistic forecast accuracy
  • Forecasting under changing operating conditions
  • Evaluating errors across wells, time horizons and production levels
  • Comparing AI forecasts with conventional methods
Day 5:Uncertainty, Deployment and Decision Support
  • Identifying geological, operational and model uncertainty
  • Developing forecast ranges and alternative production scenarios
  • Stress-testing forecasts against changing assumptions
  • Explaining model results to engineering and asset teams
  • Integrating forecasts into reservoir surveillance and planning
  • Monitoring performance and updating models as new data arrives
  • Presenting an AI-supported asset forecasting case
  • Developing a phased implementation roadmap for an upstream team

Certificate

  • 360 Leaders Training Certificate of Completion for delegates who attend and complete the training course

Would you like to take this course as a team?

AI for Upstream Oil & Gas: Reservoir Modelling and Production Forecasting FAQs

This training course enables upstream operating companies to accelerate scenario evaluation, reduce subsurface uncertainty, and improve capital allocation. By training subsurface teams to combine machine learning techniques with physics-based constraints, organisations achieve higher forecast accuracy, mitigate risk in field development plans, and maximize long-term asset recovery.  

The training course provides systematic techniques for identifying data anomalies, handling missing subsurface measurements, and aligning historical production trends with operational well events. Participants learn to separate measurement noise from genuine reservoir responses, establishing clean datasets suitable for reliable predictive modeling.  
Yes, a core focus of the learning experience is evaluating AI predictions against established engineering benchmarks, such as decline curve analysis and material balance principles. Participants learn to enforce physical domain constraints on model predictions to ensure operational viability and engineering consistency.  
Participants should possess a baseline understanding of upstream petroleum geology, reservoir engineering, or production operations. Advanced computer programming expertise is not required, as the focus remains on engineering workflows, data interpretation, feature selection, and decision support frameworks.  
The training course covers comprehensive methodologies for quantifying geological, operational, and algorithmic uncertainty. Participants explore techniques for stress-testing forecasts against variable operating parameters, water cut progression, gas-oil ratio changes, and artificial lift adjustments to build robust forecast ranges.  
Upon completion, participants will be equipped to evaluate potential digital use cases within their operational assets, prepare subsurface data for predictive modeling, validate machine learning outputs against physics-based constraints, and articulate risk-adjusted recommendations to key executive stakeholders.  

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