AI Agents for Petroleum Engineering Workflows Training Course

AI Agents for Petroleum Engineering Workflows Training Course

Introduction

The AI Agents for Petroleum Engineering Workflows Training Course provides petroleum professionals with practical knowledge and advanced skills to integrate Artificial Intelligence (AI), Agentic AI, Large Language Models (LLMs), machine learning, automation, and intelligent workflow orchestration into modern petroleum engineering operations. Participants will learn how autonomous and semi-autonomous AI agents can retrieve technical information, analyze engineering datasets, execute repetitive tasks, generate engineering insights, assist with decision-making, and coordinate multi-step workflows while maintaining appropriate human-in-the-loop controls. Emphasis is placed on practical applications of generative AI, Retrieval-Augmented Generation (RAG), digital twins, predictive analytics, natural-language interfaces, data integration, and intelligent automation within petroleum engineering environments.

The course also develops the capabilities required to design reliable AI-powered petroleum engineering workflows that improve productivity, accelerate technical analysis, enhance decision quality, and reduce repetitive engineering workload. Participants will examine agent architectures, prompt engineering, tool integration, structured engineering reasoning, data governance, model validation, cybersecurity, explainable AI, and responsible AI deployment. Through industry case studies, workflow simulations, practical exercises, and petroleum engineering scenarios, learners will explore how AI agents can assist with tasks such as production surveillance, decline-curve analysis, well diagnostics, reservoir performance monitoring, drilling optimization, artificial-lift analysis, reserves evaluation, and field-development decision support. By the end of the program, participants will be able to identify high-value AI-agent opportunities, design petroleum engineering agent workflows, evaluate their performance, and develop implementation strategies aligned with digital oilfield transformation, intelligent operations, and next-generation energy technology.

Course Duration

5 days

Course Objectives

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

1. Understand the fundamentals of Agentic AI, Generative AI, LLMs, and autonomous engineering workflows.
2. Identify high-value opportunities for AI automation in petroleum engineering operations.
3. Design AI-agent workflows for reservoir, production, drilling, and well engineering applications.
4. Apply prompt engineering and context engineering to petroleum engineering use cases.
5. Integrate AI agents with engineering databases, technical documents, APIs, spreadsheets, and analytical tools.
6. Apply Retrieval-Augmented Generation (RAG) to improve technical knowledge retrieval and engineering responses.
7. Develop AI-assisted workflows for production surveillance and well performance optimization.
8. Use AI agents to support reservoir characterization, forecasting, and reservoir management decisions.
9. Apply intelligent automation to well diagnostics, artificial lift, well testing, and production optimization.
10. Evaluate AI-agent accuracy, reliability, hallucination risks, explainability, and engineering validity.
11. Establish human-in-the-loop, AI governance, data security, and responsible AI controls.
12. Develop practical strategies for implementing AI-powered digital oilfield and petroleum engineering solutions.
13. Build an AI-agent implementation roadmap for scalable petroleum engineering workflow transformation.

Target Audience

1. Petroleum Engineers
2. Reservoir Engineers
3. Production Engineers
4. Drilling and Completion Engineers.
5. Petrophysicists and Geoscientists 
6. Digital Oilfield and Energy Digitalization Professionals
7. Petroleum Engineering Managers and Technical Leads 
8. Data Scientists, AI Engineers, and Technology Professionals

Course Modules

Module 1: Foundations of AI Agents in Petroleum Engineering

• Evolution from traditional automation and machine learning to Agentic AI.
• Understanding LLMs, Generative AI, AI agents, copilots, and autonomous workflows.
• AI-agent architecture.
• Identifying repetitive and high-value petroleum engineering workflows for AI automation.
• Case Study: Designing an AI agent to support daily petroleum engineering production surveillance.

Module 2: AI Agent Architecture and Workflow Design

• Designing single-agent and multi-agent systems for engineering workflows.
• Workflow orchestration, task decomposition, planning, and intelligent decision support.
• Connecting AI agents to databases, APIs, spreadsheets, engineering software, and document repositories.
• Designing structured inputs, outputs, validation rules, and engineering controls.
• Case Study: Developing a multi-agent workflow for integrated reservoir and production analysis.

Module 3: Prompt Engineering, Context Engineering and RAG

• Advanced prompt engineering for technical petroleum engineering applications.
• Context engineering and developing reliable engineering instructions.
• Retrieval-Augmented Generation (RAG) for technical reports, manuals, standards, and field data.
• Reducing hallucinations through grounding, source verification, and structured responses.
• Case Study: Building an AI technical assistant capable of retrieving and analyzing field engineering documentation.

Module 4: AI Agents for Reservoir Engineering

• AI-assisted reservoir characterization, material balance, and reservoir performance analysis.
• Intelligent support for decline-curve analysis and production forecasting.
• AI-assisted reservoir simulation workflow preparation and interpretation.
• Automated analysis of reservoir pressure, production, injection, and surveillance data.
• Case Study: AI agent for identifying reservoir performance trends and generating engineering recommendations.

Module 5: AI Agents for Production and Well Engineering

• Automated production surveillance and well performance monitoring.
• AI-assisted identification of production anomalies and underperforming wells.
• Intelligent workflows for artificial lift optimization and well diagnostics.
• AI-supported well testing, production allocation, and optimization workflows.
• Case Study: Developing an AI agent to prioritize wells requiring production-engineering intervention.

Module 6: AI Agents for Drilling, Completions and Field Development

• AI-assisted drilling performance monitoring and operational decision support.
• Intelligent analysis of drilling reports, daily drilling reports, and operational data.
• AI support for completion design, well planning, and technical documentation.
• AI-enabled field-development screening and scenario comparison.
• Case Study: Using AI agents to analyze drilling performance and recommend optimization opportunities.

Module 7: AI Governance, Validation, Security and Responsible Deployment

• Managing AI hallucinations, model uncertainty, bias, and incorrect engineering recommendations.
• Human-in-the-loop controls for high-impact petroleum engineering decisions.
• Data governance, cybersecurity, access control, and confidential technical information.
• AI model validation, performance monitoring, auditability, and explainable AI.
• Case Study: Creating an AI governance framework for deploying petroleum engineering agents in an operating asset.

Module 8: Building and Scaling AI-Powered Petroleum Workflows

• Selecting high-ROI use cases using AI opportunity assessment and workflow prioritization.
• Measuring productivity, engineering quality, decision speed, and business value.
• Integrating AI agents into digital oilfield, digital twin, and intelligent operations environments.
• Developing implementation, change-management, training, and adoption strategies.
• Case Study: Developing an enterprise roadmap for AI-agent deployment across reservoir, production, drilling, and field-development teams.

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.