AI for Upstream Oil & Gas: Reservoir Modelling and Production Forecasting
Applying Machine Learning to Reservoir Engineering and Forecasting
Course Schedules
Training Course Overview
AI for Upstream Oil & Gas Training Course concepts provide powerful capabilities to transform complex subsurface data into reliable operational strategies. Reservoir models and production forecasts directly guide crucial asset decisions across field development, well placement, production target setting, reserves estimation, and capital investment priorities. However, subsurface data remains inherently incomplete, reservoir behaviour carries high uncertainty, and dynamic production conditions make forecasting an ongoing engineering requirement rather than a static computation.
This Reservoir Modelling Training Course explores how machine learning models complement conventional reservoir engineering approaches. Upstream professionals will learn to prepare geological, petrophysical, well, and production data to reveal hidden performance trends and evaluate development scenarios faster. The course emphasizes data quality, feature selection, and physics-informed model validation to prevent misleading forecasts. Participants gain practical insight into deploying artificial intelligence while maintaining robust engineering interpretation, accurate uncertainty quantification, and clear communication of model assumptions for asset management.
Training Course Objectives
Production Forecasting Training Course objectives focus on equipping technical teams with actionable artificial intelligence techniques for subsurface workflows. Participants gain hands-on expertise in structuring upstream data, evaluating machine learning predictions, and integrating data science with physics-based reservoir engineering principles.
By the end of this AI for Upstream Oil & Gas Training Course, participants will be able to:
- Identify high-value upstream applications where artificial intelligence enhances reservoir characterisation and performance forecasting.
- Prepare, clean, and integrate complex subsurface, well, and dynamic production datasets for predictive modeling.
- Select critical geological, petrophysical, and operational variables to improve predictive model accuracy.
- Evaluate and compare traditional decline curve analysis and numerical simulation with machine learning approaches.
- Build, test, and validate dynamic models for well-level and field-wide production forecasting.
- Detect and prevent data leakage, overfitting, and structural bias in subsurface predictive algorithms.
- Quantify reservoir uncertainties and effectively communicate probabilistic forecast ranges to decision-makers.
- Combine machine learning outputs with fundamental reservoir physics, material balance, and domain expertise.
- Formulate a structured implementation plan and business case for an upstream AI initiative.
Designed for
Reservoir Modelling Training Course delegates include subsurface specialists, production managers, and digital transformation teams aiming to leverage machine learning for field optimization. This comprehensive training enhances technical capabilities across technical and operational departments without requiring advanced programming experience.
This AI for Upstream Oil & Gas Training Course will greatly benefit:
- Reservoir engineers seeking to accelerate scenario evaluation using data-driven proxy models.
- Petroleum and production engineers looking to optimize well performance and rate forecasts.
- Geoscientists and petrophysicists interested in integrating spatial subsurface data with predictive analytics.
- Field development and asset management teams responsible for long-term production and capital planning.
- Upstream data scientists and data analysts aiming to align machine learning tools with domain physics.
- Digital transformation leaders driving practical AI adoption across upstream technical operations.
- Asset managers and technical leaders relying on robust production forecasts for investment decisions.
Learning Methods
Production Forecasting Training Course delivery combines interactive technical lectures, real-world oil and gas case studies, guided data exercises, and practical group discussions. Delegates work with representative subsurface and production datasets to prepare inputs, build predictive models, assess forecast precision, and present data-backed recommendations for asset management decisions. Exercises demonstrate how machine learning algorithms compare against conventional reservoir engineering tools, giving participants immediate practical knowledge without requiring prior programming experience.
Course Content
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
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
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
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
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
The Certificate
- Anderson Certificate of Completion for delegates who attend and complete the training course
In Partnership With
Learn more about this course
The course is suitable for mechanical maintenance engineers, reliability engineers, maintenance technicians, field service engineers, plant operators, production supervisors, condition monitoring specialists, and technical personnel responsible for industrial hydraulic machinery and fluid power equipment.
Yes. Participants who successfully complete the AI for Upstream Oil & Gas: Reservoir Modelling and Production Forecasting training course will receive a Anderson Certificate of Completion, demonstrating their commitment to professional development and continuous learning. This certificate provides formal recognition of the knowledge and skills gained during the course and can support professional growth and career progression.
Yes. The AI for Upstream Oil & Gas: Reservoir Modelling and Production Forecasting training course can be customised and delivered exclusively for organisations seeking a tailored learning solution. Course content can be adapted to address specific business objectives, operational challenges, industry requirements, and organisational priorities. Customised training allows teams to focus on the topics most relevant to their roles while supporting wider organisational development goals.
Participants attending the AI for Upstream Oil & Gas: Reservoir Modelling and Production Forecasting training course gain access to valuable industry insights, practical techniques, and internationally recognised best practices. The course helps professionals improve performance, strengthen confidence, broaden their perspective, and develop skills that contribute to both personal and organisational success. It also provides an excellent opportunity to exchange ideas and experiences with professionals from diverse sectors and backgrounds.
No. The AI for Upstream Oil & Gas: Reservoir Modelling and Production Forecasting training course is open to professionals from a wide range of backgrounds and experience levels. The course content is structured to provide value to both those who are new to the subject and experienced practitioners seeking to deepen their expertise. While some prior knowledge may enhance understanding of certain concepts, it is not a requirement for participation
The AI for Upstream Oil & Gas: Reservoir Modelling and Production Forecasting training course combines practical knowledge, current industry practices, and expert guidance to create a highly relevant learning experience. Rather than focusing solely on theory, the course emphasises practical application, enabling participants to develop skills and approaches that can be implemented directly within their organisations. This balance of knowledge and practical relevance helps participants achieve meaningful and lasting professional impact.
The AI for Upstream Oil & Gas: Reservoir Modelling and Production Forecasting training course uses a variety of learning approaches to maximise participant engagement and knowledge retention. These may include expert-led presentations, practical exercises, case studies, group discussions, scenario-based activities, and collaborative learning opportunities. This approach encourages active participation and helps participants translate learning into practical workplace results.
Yes. Industrial hydraulic systems are widely used across manufacturing plants, process facilities, heavy machinery, marine operations, utilities, and other industrial environments. The principles covered are relevant to professionals responsible for operating, maintaining, inspecting, or troubleshooting hydraulic equipment across a broad range of applications.
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