Course Finder
2026 Classroom Training Courses 2026 Online Training Courses 2027 Classroom Training Courses 2027 OnlineTraining Courses
In-House Training About Us
An Intensive Professional
Development Training Course on

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

Integrating Subsurface Data, Machine Learning and Engineering Judgement

Course Options

How would you like to proceed?

Need a tailored solution or group booking discount? Contact our course advisors.

Why Choose Us

The AI for Upstream Oil & Gas: Reservoir Modelling and Production Forecasting training course explains how AI can complement established engineering methods in practical upstream decision-making. Reservoir models and forecasts inform development plans, well placement, production targets, reserves estimates and investment priorities throughout an asset’s life. However, subsurface information remains incomplete, reservoir behaviour is uncertain, and changing operating conditions can influence production data. Production forecasting must therefore remain an ongoing engineering activity rather than a single calculation.

AI and machine learning enable upstream teams to examine substantial volumes of geological, petrophysical, well and production data. These methods can identify patterns, produce alternative forecasts and accelerate the evaluation of development scenarios. Their value relies on relevant, high-quality input data, appropriate validation and a robust understanding of reservoir physics. A close match with historical production does not guarantee reliable performance under different operating conditions or when applied to another well. Participants progress from data preparation and feature selection through model development, uncertainty assessment and decision support, while interpreting results against conventional methods and communicating assumptions and limitations clearly.

This AI for Upstream Oil & Gas: Reservoir Modelling and Production Forecasting training course will highlight:

  • Analysing geological, petrophysical, well and production data
  • Preparing data and selecting relevant model features
  • Developing and appropriately validating AI forecasting models
  • Comparing AI applications with conventional engineering methods
  • Assessing forecast uncertainty and changing operating conditions
  • Communicating forecast assumptions, limitations and decision implications
Why Choose AI for Upstream Oil & Gas: Reservoir Modelling and Production Forecasting
Course Goals

What are the Goals?

Upon completing this course, participants will be able to:

  • Recognise upstream uses in which AI can assist with reservoir modelling and forecasting.
  • Prepare and evaluate subsurface, well and production data for analysis.
  • Choose applicable geological, reservoir and operational variables.
  • Contrast traditional forecasting techniques with AI-driven methods.
  • Build and assess models for forecasting well and field production.
  • Identify data leakage, overfitting and additional causes of misleading outcomes.
  • Evaluate uncertainty and present forecast ranges.
  • Integrate AI outputs with reservoir engineering expertise and physical constraints.
  • Establish an implementation plan for an upstream AI use case.

Who is this Training Course for?

Who Should Attend?

This AI for Upstream Oil & Gas: Reservoir Modelling and Production Forecasting training course is designed for:

  • Reservoir Engineers
  • Petroleum Engineers
  • Production Engineers
  • Reservoir Modelling Specialists
  • Production Forecasting Specialists
  • Reservoir Simulation Engineers
  • Upstream Data Scientists
  • Petroleum Data Analysts
  • Geoscientists
  • Subsurface Engineers
  • Field Development Engineers
  • Upstream Digital Transformation Specialists
Learning Approach

How will this Training Course be Presented?

This course uses technical presentations, upstream case studies, group discussions and guided data exercises. Participants analyse a representative reservoir and production dataset, evaluate different forecasting approaches, assess model performance and deliver a recommendation for an asset decision. Advanced programming experience is not necessary to understand the course concepts.

The 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
Recognition

Certificate

  • • Wallstreet Development Academy Certificate of Completion for delegates who attend and complete the training course.

Frequently Asked Questions

The course focuses on applying AI to reservoir modelling and production forecasting in upstream oil and gas. Its stated scope brings together subsurface data, machine learning and engineering judgement. More detailed modules or technical methods cannot be verified because no course overview or syllabus has been provided.
Engineering judgement is presented as an integral part of applying AI to subsurface analysis. The subtitle indicates that machine learning is considered alongside domain knowledge rather than as a standalone approach. However, the course page content provided does not specify particular decision frameworks, validation procedures or case studies.
Production forecasting is one of the two central technical applications named in the course title. The AI for Upstream Oil & Gas training course places it within an approach that combines subsurface data, machine learning and engineering judgement. No further details are available to verify the forecasting techniques, time horizons or production variables covered.
It is relevant because it addresses AI within two defined upstream applications: reservoir modelling and production forecasting. The course also highlights the need to integrate machine learning with subsurface data and engineering judgement, keeping the focus on domain-specific use rather than generic AI. Additional claims about benefits, performance improvements or business outcomes cannot be verified from the provided content.
You will explore the integration of subsurface data with machine learning in the context of upstream reservoir and production work. The course title indicates that this integration is connected to reservoir modelling and production forecasting rather than treated as a general AI topic. The specific datasets, algorithms and software used cannot be verified from the available course information.
The training is designed to connect AI and machine learning with reservoir modelling tasks in upstream oil and gas. Its emphasis on subsurface data and engineering judgement suggests a practical link between data-driven analysis and reservoir understanding. Specific modelling workflows, tools and reservoir scenarios cannot be confirmed from the information supplied.
The course is intended to support upstream oil and gas work involving reservoir modelling and production forecasting. Its stated integrated approach is relevant where subsurface information, machine-learning outputs and engineering judgement must be considered together. The available material does not identify specific job roles, workplace exercises or implementation tools.
If you would like further information about this training course, our team is available to provide professional guidance and support: Email: [email protected] In-house training: https://www.wallstreetda.com/in-house-training

Related Articles

View All Articles