Physics-Informed AI for Petroleum Engineering Training Course

Physics-Informed AI for Petroleum Engineering Training Course

Introduction

Physics-Informed AI for Petroleum Engineering Training Course is designed to equip petroleum engineers, data scientists, reservoir specialists, and energy professionals with advanced skills in integrating artificial intelligence (AI), machine learning (ML), physics-informed neural networks (PINNs), computational modeling, and petroleum engineering principles. This specialized training course explores how physics-informed machine learning combines governing physical laws, mathematical equations, geological knowledge, and operational data to solve complex petroleum engineering challenges. Participants will learn how to apply deep learning, scientific machine learning, reservoir simulation, fluid flow modeling, digital twins, and predictive analytics to subsurface characterization, reservoir performance forecasting, well optimization, and production engineering. The course emphasizes the practical integration of physical principles, including conservation of mass, momentum, and energy, into AI models to improve their consistency, interpretability, and generalization when working with limited, noisy, or incomplete field data.

The program covers reservoir pressure prediction, multiphase flow simulation, production optimization, history matching, well performance analysis, reservoir parameter estimation, uncertainty quantification, and intelligent decision support. Participants will explore how physics-informed neural networks can complement conventional numerical simulators, accelerate computationally intensive workflows, and improve understanding of complex subsurface systems. Additional emphasis is placed on model validation, data quality, computational efficiency, explainable AI, and the responsible implementation of AI technologies in operational environments. By the end of the course, participants will be better positioned to develop hybrid physics- and data-driven models, evaluate emerging AI technologies, and translate scientific machine learning innovations into measurable improvements in petroleum recovery, production efficiency, reservoir management, and long-term asset value.

Course Duration

5 days

Course Objectives

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

1. Explain the fundamentals of physics-informed artificial intelligence and its applications in petroleum engineering.
2. Apply physics-informed neural networks (PINNs) to solve petroleum engineering problems governed by differential equations.
3. Integrate machine learning algorithms with reservoir engineering principles and physical constraints.
4. Develop AI models incorporating mass, momentum, and energy conservation laws.
5. Apply scientific machine learning techniques to reservoir characterization and subsurface modeling.
6. Improve reservoir simulation workflows using hybrid physics-based and data-driven approaches.
7. Use physics-informed AI for reservoir pressure prediction and fluid flow analysis.
8. Apply deep learning to well performance forecasting and production optimization.
9. Develop surrogate models to accelerate computationally expensive reservoir simulations.
10. Implement physics-informed techniques for history matching and reservoir parameter estimation.
11. Evaluate model accuracy, robustness, interpretability, and generalization under limited-data conditions.
12. Incorporate uncertainty quantification and sensitivity analysis into AI-assisted petroleum engineering decisions.
13. Design practical AI-driven digital twin and predictive analytics solutions for petroleum asset management.

Target Audience

1. Petroleum engineers and reservoir engineers.
2. Production engineers and well performance specialists.
3. Geoscientists and reservoir characterization professionals.
4. Petroleum data scientists and machine learning engineers.
5. Reservoir simulation and computational modeling specialists.
6. Digital transformation, automation, and AI professionals in oil and gas.
7. Research scientists and engineering consultants working on scientific machine learning.
8. Technical managers and decision-makers responsible for digital oilfield and reservoir optimization projects.

Course Modules

Module 1: Fundamentals of Physics-Informed AI in Petroleum Engineering

• Introduction to artificial intelligence, machine learning, and scientific machine learning.
• Principles of physics-informed neural networks and hybrid AI modeling.
• Differences between purely data-driven, physics-based, and hybrid models.
• Governing equations in petroleum engineering and their role in AI development.
• Case study: Comparing conventional machine learning and physics-informed models for reservoir pressure prediction.

Module 2: Mathematical and Physical Foundations for PINNs

• Partial differential equations and their applications in petroleum engineering.
• Conservation of mass, momentum, and energy in subsurface systems.
• Boundary conditions, initial conditions, and constitutive relationships.
• Automatic differentiation and physics-based loss function development.
• Case study: Solving a simplified pressure diffusion equation using a physics-informed neural network.

Module 3: Physics-Informed AI for Reservoir Characterization

• Integration of geological, petrophysical, and production datasets.
• AI-based estimation of porosity, permeability, and reservoir properties.
• Incorporating geological constraints into machine learning models.
• Modeling spatial variability and heterogeneous reservoir characteristics.
• Case study: Predicting permeability distribution in a heterogeneous reservoir using limited well-log data and physical constraints.

Module 4: Reservoir Simulation and Physics-Informed Flow Modeling

• Fundamentals of single-phase and multiphase flow in porous media.
• Incorporating Darcy's law and fluid transport equations into AI models.
• Physics-informed modeling of pressure propagation and fluid saturation.
• Hybrid AI and numerical reservoir simulation workflows.
• Case study: Modeling pressure and saturation evolution in a simplified waterflood reservoir.

Module 5: Physics-Informed AI for Well and Production Engineering

• Modeling well inflow performance and pressure losses.
• AI-assisted prediction of oil, gas, and water production rates.
• Incorporating fluid properties and flow constraints into production models.
• Detecting abnormal production behavior using physics-informed analytics.
• Case study: Developing a constrained AI model to forecast production decline and identify inconsistent predictions in a producing well.

Module 6: AI-Driven History Matching and Reservoir Optimization

• Fundamentals of reservoir history matching and parameter calibration.
• Using physics-informed learning to estimate uncertain reservoir parameters.
• Integrating production history and pressure measurements into model training.
• Optimizing injection rates, well placement, and production strategies.
• Case study: Calibrating a simplified reservoir model against historical production data and comparing the results with a conventional calibration workflow.

Module 7: Advanced PINNs, Surrogate Modeling, and Digital Twins

• Designing deep neural network architectures for scientific machine learning.
• Developing surrogate models for computationally intensive reservoir simulations.
• Integrating physics-informed AI with reservoir digital twins.
• Model generalization, computational acceleration, and deployment considerations.
• Case study: Building a prototype digital twin that uses a physics-informed surrogate model to evaluate alternative reservoir operating scenarios.

Module 8: Model Validation, Uncertainty Quantification, and Industry Deployment

• Evaluating predictive accuracy using appropriate engineering performance metrics.
• Conducting sensitivity analysis and uncertainty quantification.
• Addressing sparse data, noisy measurements, and model overfitting.
• Applying explainable AI, model governance, and engineering validation.
• Case study: Assessing the reliability of a physics-informed production forecast under uncertain permeability, pressure, and fluid-property assumptions.

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.