Production Optimization Using Real-Time Data Training Course

Production Optimization Using Real-Time Data Training Course

Introduction

Production Optimization Using Real-Time Data Training Course is designed to equip oil and gas professionals with the advanced technical knowledge and practical analytical skills required to optimize hydrocarbon production through real-time monitoring, data-driven decision-making, and intelligent production management. This training course explores how real-time data from production sensors, smart wells, SCADA systems, distributed control systems, flow meters, pressure transmitters, and other digital oilfield technologies can be transformed into actionable insights. Participants will learn to interpret dynamic production parameters, identify performance deviations, detect production bottlenecks, evaluate well behavior, and implement proactive interventions that enhance production reliability, reduce operating costs, and improve reservoir recovery. The program also emphasizes real-time production surveillance, intelligent well optimization, automated anomaly detection, production forecasting, and advanced petroleum data analytics to support effective decision-making across upstream oil and gas operations.

The course examines how integrated production dashboards, cloud-based analytics, edge computing, digital twins, and AI-powered optimization platforms support continuous performance improvement across wells, gathering systems, and production facilities. Participants will explore techniques for identifying declining well performance, optimizing choke settings, monitoring water and gas production, evaluating pressure and temperature trends, and improving production allocation using timely and reliable operational data. The training also addresses data quality management, cybersecurity considerations, alarm management, workflow automation, and the integration of engineering expertise with digital decision-support systems. By the end of the program, delegates will be better equipped to implement data-driven production optimization strategies, enhance production uptime, maximize hydrocarbon output, reduce non-productive time, and improve asset profitability in increasingly digital and competitive petroleum operations.

Course Duration

5 days

Course Objectives

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

1. Understand the principles of real-time production optimization and their application in modern oil and gas operations.
2. Interpret real-time production data using advanced production surveillance and performance monitoring techniques.
3. Integrate data from SCADA, distributed control systems, smart sensors, and digital oilfield platforms.
4. Apply AI-driven analytics and machine learning to identify production inefficiencies and emerging operational risks.
5. Identify production bottlenecks through real-time analysis of pressure, temperature, flow rate, and fluid properties.
6. Optimize well production performance using dynamic data interpretation and engineering diagnostics.
7. Improve artificial lift efficiency through continuous monitoring of ESP, gas lift, and rod pump systems.
8. Apply predictive maintenance analytics to anticipate equipment failures and minimize unplanned downtime.
9. Develop and interpret real-time production dashboards, KPIs, alerts, and operational performance indicators.
10. Use digital twin technology and scenario analysis to evaluate production optimization opportunities.
11. Improve production forecasting and short-term output predictions using real-time operational trends.
12. Implement data quality assurance, anomaly detection, and reliable production data management practices.
13. Develop an actionable digital production optimization strategy to improve recovery, operational efficiency, and asset value.

Target Audience

1. Petroleum and Production Engineers.
2. Reservoir Engineers and Reservoir Management Specialists.
3. Well Performance and Production Surveillance Engineers.
4. Artificial Lift and Well Intervention Specialists.
5. Oilfield Operations and Production Supervisors.
6. SCADA, Instrumentation, Automation, and Control Engineers.
7. Data Scientists, Petroleum Data Analysts, and Digital Oilfield Specialists.
8. Asset Managers, Operations Managers, and Technical Decision-Makers in the oil and gas industry.

Course Modules

Module 1: Fundamentals of Real-Time Production Optimization

• Principles and objectives of real-time production optimization in upstream petroleum operations.
• Digital oilfield architecture and the evolution of intelligent production systems.
• Key production parameters: flow rate, pressure, temperature, water cut, and gas-oil ratio.
• Production optimization workflows, operational KPIs, and continuous improvement frameworks.
• Case study: Evaluating a mature oilfield where declining production is identified through continuous well-performance monitoring.

Module 2: Real-Time Data Acquisition and Digital Oilfield Integration

• Data acquisition from smart sensors, flow meters, pressure gauges, and temperature transmitters.
• SCADA systems, distributed control systems, and supervisory production monitoring.
• Integration of wellhead, downhole, artificial lift, and surface-facility data.
• Data transmission, edge computing, cloud platforms, and industrial connectivity.
• Case study: Integrating real-time data from multiple producing wells into a unified production surveillance dashboard.

Module 3: Production Surveillance and Performance Analytics

• Real-time monitoring of well production rates and operational performance.
• Analysis of pressure, temperature, fluid composition, and water production trends.
• Production allocation, reconciliation, and identification of measurement inconsistencies.
• KPI development, dashboard interpretation, threshold setting, and exception-based surveillance.
• Case study: Detecting an unexplained decline in oil production through trend analysis and well-level performance comparisons.

Module 4: AI, Machine Learning, and Predictive Production Analytics

• Applications of artificial intelligence in petroleum production optimization.
• Machine learning techniques for identifying abnormal production behavior.
• Predictive analytics for production decline, equipment degradation, and operational constraints.
• Automated anomaly detection, intelligent alerts, and decision-support algorithms.
• Case study: Using historical and real-time sensor data to identify early warning signs of artificial lift underperformance.

Module 5: Real-Time Well Optimization and Artificial Lift Management

• Optimization of choke settings and operating conditions using live well data.
• Monitoring and optimization of electric submersible pumps (ESPs).
• Gas lift performance analysis and injection-rate optimization.
• Rod pump surveillance, operating envelope assessment, and failure indicators.
• Case study: Improving production from a group of artificially lifted wells by identifying inefficient operating conditions and evaluating alternative settings.

Module 6: Production Bottleneck Identification and Operational Troubleshooting

• Identifying flow restrictions across wells, pipelines, separators, and surface facilities.
• Diagnosing pressure drops, flow instability, slugging, and changing fluid conditions.
• Real-time water-cut and gas-oil-ratio monitoring for production diagnostics.
• Root-cause analysis, exception management, and prioritization of corrective actions.
• Case study: Investigating reduced field output caused by a constrained gathering system and coordinating corrective action using live operating data.

Module 7: Digital Twins, Forecasting, and Automated Optimization

• Fundamentals of digital twins for wells, production networks, and processing facilities.
• Integrating real-time data with production models and engineering simulations.
• Short-term production forecasting and scenario-based optimization.
• Automated recommendations, constrained optimization, and engineering validation.
• Case study: Using a simulated digital twin of a production network to assess the effects of changing operating conditions on total field output.

Module 8: Implementation, Governance, and Continuous Improvement

• Developing a real-time production optimization implementation roadmap.
• Data governance, validation, sensor reliability, and management of missing data.
• Cybersecurity, access control, alarm management, and operational technology risks.
• Measuring optimization outcomes through production gains, uptime, energy efficiency, and operating costs.
• Case study: Designing a digital production optimization pilot for a mature field, including KPIs, implementation priorities, and a performance review framework.

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.