Digital Production Optimization Training Course

Digital Production Optimization Training Course

Introduction

Digital Production Optimization Training Course provides a comprehensive and practical framework for applying digital technologies, real-time production data, advanced analytics, artificial intelligence (AI), machine learning (ML), predictive analytics, and automation to optimize oil and gas production systems. As petroleum operations become increasingly data-driven, production engineers and asset teams need the ability to transform high-frequency operational data into actionable insights that improve well performance, production efficiency, asset reliability, artificial lift performance, flow assurance, and overall production economics. This course explores how digital production workflows can integrate data from SCADA, historians, IoT sensors, smart wells, digital twins, production allocation systems, and field surveillance platforms to identify production constraints and optimize operating conditions.

Participants will develop practical skills in real-time production monitoring, digital well surveillance, production forecasting, anomaly detection, predictive maintenance, workflow automation, and data-driven decision-making. Through industry-based exercises and case studies, the course demonstrates how digital optimization can increase hydrocarbon recovery, reduce downtime, minimize operating costs, improve energy efficiency, and support integrated asset management. Particular attention is given to combining petroleum engineering fundamentals with emerging technologies such as AI-powered optimization, digital twins, cloud analytics, edge computing, intelligent automation, and machine learning, enabling participants to build sustainable and scalable digital production optimization strategies.

Course Duration

5 days

Course Objectives

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

1. Understand the principles and applications of Digital Production Optimization in modern oil and gas operations.
2. Apply real-time production analytics to identify performance trends, constraints, and optimization opportunities.
3. Integrate SCADA, IoT, historian, and production data for improved operational decision-making.
4. Use AI and machine learning techniques to support production optimization and anomaly detection.
5. Develop digital well surveillance workflows for continuous monitoring of production performance.
6. Apply predictive analytics to identify potential production losses and equipment failures.
7. Evaluate and optimize artificial lift systems using real-time operational data.
8. Build and apply digital twin concepts for production system monitoring and optimization.
9. Identify production bottlenecks using data-driven root cause analysis and constraint management.
10. Improve production forecasting through advanced analytics and predictive modeling.
11. Develop automated workflows for production surveillance and optimization decisions.
12. Evaluate optimization opportunities based on production economics, energy efficiency, and operational risk.
13. Design a practical digital production optimization strategy for an oil and gas asset.

Target Audience

1. Production Engineers and Petroleum Engineers
2. Reservoir and Well Performance Engineers
3. Operations and Production Supervisors
4. Artificial Lift Engineers and Specialists
5. Digital Oilfield and Digital Transformation Professionals
6. Data Scientists, Data Analysts, and AI/ML Professionals in Energy
7. Asset Managers and Petroleum Operations Managers
8. Technical Consultants and Energy Industry Decision-Makers

Course Modules

Module 1: Fundamentals of Digital Production Optimization

• Digital transformation in modern oil and gas production
• Production optimization principles and workflows
• Digital oilfield architecture and integrated operations
• Production data sources, quality, and governance
• Case Study: Digital transformation of a mature producing field

Module 2: Real-Time Production Data and Digital Surveillance

• SCADA, IoT sensors, historians, and real-time data streams
• Production data acquisition and visualization
• Real-time well and facility performance monitoring
• Data quality, validation, and anomaly identification
• Case Study: Real-time surveillance for identifying declining well performance

Module 3: Data Analytics for Production Optimization

• Descriptive, diagnostic, predictive, and prescriptive analytics
• Production trend analysis and performance benchmarking
• Identification of production losses and operational constraints
• Data-driven production opportunity ranking
• Case Study: Using production analytics to recover lost field production

Module 4: AI and Machine Learning for Production Optimization

• Machine learning applications in petroleum production
• Supervised and unsupervised learning techniques
• Anomaly detection and pattern recognition
• Predictive models for production performance
• Case Study: Machine learning model for detecting abnormal well behavior

Module 5: Digital Twins and Intelligent Production Systems

• Digital twin architecture for oil and gas assets
• Dynamic modeling of wells and production facilities
• Real-time model calibration and performance monitoring
• Scenario analysis and virtual optimization
• Case Study: Digital twin application for optimizing a multi-well production system

Module 6: Artificial Lift and Well Performance Optimization

• Digital monitoring of ESP, gas lift, and rod pump systems
• Artificial lift performance analytics
• Identifying lift-related production constraints
• Predictive maintenance and failure forecasting
• Case Study: AI-assisted optimization of ESP operating conditions

Module 7: Automated Optimization, Forecasting and Decision Support

• Automated production allocation and surveillance
• Production forecasting using advanced analytics
• Optimization algorithms and decision-support systems
• Automated alerts, workflows, and recommended actions
• Case Study: Automated optimization workflow for a constrained offshore asset

Module 8: Integrated Digital Production Strategy and Economics

• Integrating wells, reservoirs, facilities, and operations data
• Production optimization economics and value generation
• Energy efficiency and emissions-aware production optimization
• Digital implementation roadmaps and KPI development
• Case Study: Building a digital production optimization program for a mature asset

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.