Autonomous Production Operations Training Course

Autonomous Production Operations Training Course

Introduction

Autonomous Production Operations Training Course is a comprehensive professional development program designed to equip oil and gas professionals with the technical knowledge and practical competencies required to implement, manage, and optimize autonomous production systems across upstream oil and gas facilities. Participants will learn how autonomous production technologies support continuous monitoring, automated decision-making, equipment performance optimization, production forecasting, and adaptive process control across oilfields, offshore platforms, gas processing plants, and other critical energy infrastructure. 

Participants will examine how real-time sensor data, supervisory control and data acquisition (SCADA), distributed control systems (DCS), programmable logic controllers (PLCs), and advanced analytics can be integrated to improve production stability, optimize artificial lift performance, detect abnormal operating conditions, and reduce unplanned downtime. By examining real-world operational scenarios, learners will gain insights into developing autonomous production roadmaps, evaluating automation readiness, measuring key performance indicators (KPIs), and aligning digital initiatives with production targets, environmental objectives, and regulatory requirements. This training course is suitable for organizations seeking to advance smart field development, accelerate industrial digitalization, improve production asset integrity, and establish resilient, efficient, and future-ready autonomous production operations.

Course Duration

5 Days

Course Objectives

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

1. Explain the principles, technologies, and business benefits of autonomous production operations in the modern oil and gas industry.
2. Evaluate digital oilfield maturity and identify opportunities for intelligent automation across production assets.
3. Apply artificial intelligence and machine learning concepts to production optimization and operational decision-making.
4. Integrate IIoT sensors, SCADA, DCS, and industrial communication systems for real-time production monitoring.
5. Develop autonomous process control strategies that improve production stability and operational efficiency.
6. Apply predictive maintenance and condition-monitoring techniques to minimize equipment failures and unplanned downtime.
7. Optimize smart well performance through real-time surveillance, automated diagnostics, and intelligent control.
8. Use digital twins and physics-based models to simulate production scenarios and improve asset performance.
9. Implement anomaly detection and root-cause analysis for early identification of abnormal production conditions.
10. Evaluate advanced analytics and automated production allocation techniques for improved operational visibility.
11. Strengthen OT cybersecurity, data governance, and risk management within autonomous production environments.
12. Establish performance KPIs for measuring reliability, production efficiency, energy consumption, and automation effectiveness.
13. Develop an actionable roadmap for implementing scalable, safe, and sustainable autonomous production operations.

Target Audience

1. Production engineers and petroleum engineers.
2. Oilfield operations supervisors and production managers.
3. Automation, instrumentation, and control engineers.
4. Digital oilfield and industrial digital transformation specialists.
5. Artificial intelligence, machine learning, and data analytics professionals in energy.
6. Reliability, maintenance, and asset integrity engineers.
7. SCADA, DCS, PLC, and operational technology specialists.
8. Oil and gas project managers, technical consultants, and energy technology decision-makers.

Course Modules

Module 1: Fundamentals of Autonomous Production Operations

• Definition, evolution, and strategic value of autonomous production operations.
• Differences between conventional automation, remote operations, and autonomous systems.
• Key components of smart fields and digitally enabled production facilities.
• Levels of autonomy, supervisory control, and human-in-the-loop decision-making.
• Business cases for production optimization, cost reduction, safety, and asset reliability.
• Case Study: Evaluating the transition of a conventional oil production facility into a digitally connected operation, identifying suitable automation opportunities, operational risks, and expected efficiency improvements.

Module 2: Digital Oilfield Architecture and Industrial Connectivity

• Digital oilfield architecture and integrated production management systems.
• IIoT sensors, smart instruments, and real-time data acquisition.
• SCADA, DCS, PLCs, and distributed industrial control architectures.
• Industrial communication protocols, edge computing, and cloud integration.
• Data integration, historian systems, and centralized operations centers.
• Case Study: Designing a connected production monitoring architecture for a multiwell oilfield that integrates field instrumentation, SCADA, production historians, and a remote operations center.

Module 3: Artificial Intelligence and Advanced Production Analytics

• Applications of AI and machine learning in autonomous production operations.
• Real-time production data analysis and intelligent decision support.
• Predictive analytics for production forecasting and performance optimization.
• Anomaly detection, fault classification, and automated event identification.
• Model validation, data quality, explainability, and continuous improvement.
• Case Study: Developing an AI-assisted surveillance workflow that identifies abnormal production trends, prioritizes wells for investigation, and recommends appropriate engineering reviews.

Module 4: Autonomous Process Control and Production Optimization

• Advanced process control and automated operating setpoints.
• Model predictive control for production and processing facilities.
• Automated choke management and production parameter optimization.
• Intelligent control of pressure, temperature, flow rates, and separation systems.
• Safe operating envelopes, control overrides, and escalation procedures.
• Case Study: Assessing an automated production control strategy for a facility experiencing fluctuating inlet pressure and unstable separator performance, with the aim of improving process stability while maintaining safe operating limits.

Module 5: Smart Wells and Autonomous Artificial Lift Management

• Smart well architecture and intelligent completion technologies.
• Real-time well surveillance and automated performance diagnostics.
• AI-supported artificial lift monitoring and optimization.
• Intelligent management of electric submersible pumps and gas lift systems.
• Automated detection of declining well performance and abnormal operating conditions.
• Case Study: Analyzing production and equipment data from a group of artificially lifted wells to identify underperforming assets and develop an optimization plan supported by engineering validation.

Module 6: Predictive Maintenance and Autonomous Asset Reliability

• Condition-based maintenance and predictive maintenance strategies.
• Equipment health monitoring using vibration, temperature, and pressure data.
• Machine learning applications for failure prediction and anomaly detection.
• Integration of asset performance management with computerized maintenance systems.
• Reliability KPIs, maintenance prioritization, and reduction of unplanned downtime.
• Case Study: Implementing a predictive maintenance framework for production pumps and compressors using equipment sensor trends, maintenance histories, and performance indicators to prioritize inspections and reduce unexpected outages.

Module 7: Digital Twins, Cybersecurity, and Operational Risk

• Digital twin technologies for production facilities and process equipment.
• Real-time simulation, scenario analysis, and virtual commissioning.
• OT cybersecurity principles for connected and autonomous production systems.
• Functional safety, alarm management, and autonomous control safeguards.
• Data governance, access control, system resilience, and incident response.
• Case Study: Evaluating a digital twin and OT cybersecurity improvement project for an offshore production platform, focusing on model accuracy, access management, safe control boundaries, and operational resilience.

Module 8: Autonomous Operations Implementation and Performance Management

• Assessing organizational and technological readiness for autonomy.
• Developing autonomous production implementation roadmaps.
• Integration of engineering teams, control rooms, and remote operations centers.
• KPI dashboards for production efficiency, reliability, energy performance, and cost.
• Change management, workforce competency development, and continuous optimization.
• Case Study: Developing a phased autonomous operations roadmap for a mature oilfield, including pilot selection, technology integration, operational risk assessment, performance targets, and a plan for scaling successful solutions.

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.