Artificial Lift Surveillance and Performance Analysis Training Course

Artificial Lift Surveillance and Performance Analysis Training Course

Introduction

Artificial Lift Surveillance and Performance Analysis Training Course is a specialized professional development program designed to equip oil and gas professionals with advanced technical knowledge and practical skills in artificial lift system monitoring, production optimization, well performance evaluation, and real-time surveillance. This training course explores the principles, technologies, and operational strategies behind Electrical Submersible Pumps (ESP), Gas Lift, Rod Pumping, Progressive Cavity Pumps (PCP), and Hydraulic Pumping Systems. The course also incorporates digital oilfield technologies, predictive analytics, machine learning, automated surveillance, and real-time production monitoring to help participants understand modern approaches to artificial lift performance management.

The program also addresses the integration of supervisory control and data acquisition (SCADA), production historians, well surveillance dashboards, and condition-based monitoring systems into everyday production workflows. By combining conventional petroleum engineering principles with data-driven decision-making, reliability-centered maintenance, digital twin concepts, and asset performance optimization, the course helps organizations improve well availability, reduce unplanned downtime, extend equipment run life, and optimize operating expenditure. Participants will gain practical insights into building effective surveillance programs that support production targets, operational safety, and long-term reservoir management.

Course Duration

5 Days

Course Objectives

1. Explain the operating principles, applications, and performance characteristics of major artificial lift technologies.
2. Develop effective artificial lift surveillance strategies for individual wells and multiwell production assets.
3. Interpret real-time production data, pressure trends, temperature profiles, and equipment operating parameters.
4. Evaluate artificial lift performance using production rates, drawdown, pump efficiency, and system operating envelopes.
5. Identify abnormal operating conditions, production deferment, and early-warning indicators of equipment failure.
6. Apply root cause analysis (RCA) and systematic troubleshooting techniques to artificial lift performance problems.
7. Analyze ESP performance using motor current, vibration indicators, pump intake pressure, and discharge pressure.
8. Assess gas lift performance through injection rates, injection pressure, valve behavior, and production response.
9. Evaluate rod pump and PCP performance using dynamometer data, pump fillage, torque, and operating trends.
10. Use production analytics and key performance indicators (KPIs) to benchmark artificial lift system reliability and efficiency.
11. Integrate SCADA systems, production historians, and digital surveillance dashboards into well monitoring workflows.
12. Apply predictive maintenance, anomaly detection, and data-driven forecasting concepts to reduce equipment failures.
13. Recommend artificial lift optimization measures that improve production reliability, energy efficiency, equipment longevity, and operating economics.

Target Audience

1. Petroleum Engineers and Production Engineers.
2. Artificial Lift Engineers and Specialists.
3. Well Surveillance and Production Optimization Engineers.
4. ESP, Gas Lift, Rod Pumping, and PCP Engineers.
5. Field Production Operators and Well Services Personnel.
6. Reliability, Maintenance, and Asset Integrity Engineers.
7. Digital Oilfield, SCADA, and Production Data Analysts.
8. Oil and Gas Operations Managers, Technical Supervisors, and Asset Managers.

Course Modules

Module 1: Fundamentals of Artificial Lift Systems

• Principles of artificial lift and its role in sustaining oil and gas production.
• Overview of ESP, gas lift, rod pumping, PCP, and hydraulic pumping technologies.
• Reservoir inflow performance, well productivity, and artificial lift requirements.
• Key operating parameters, production constraints, and system operating envelopes.
• Artificial lift selection criteria based on well conditions, fluid properties, and lifecycle economics.
• Case Study: Evaluate a mature oilfield with declining reservoir pressure and determine suitable artificial lift options for wells with different production rates, water cuts, and gas-to-oil ratios.

Module 2: Artificial Lift Surveillance Frameworks and Workflows

• Design of structured daily, weekly, and monthly well surveillance programs.
• Identification of critical surveillance parameters and data collection requirements.
• Establishment of operating limits, alarm thresholds, and performance baselines.
• Development of well surveillance dashboards and exception-based monitoring workflows.
• Prioritization of wells according to production risk, equipment condition, and economic impact.
• Case Study: Develop a surveillance workflow for a 50-well producing asset, using exception-based reporting to identify wells requiring immediate engineering review.

Module 3: Production Data Analysis and Performance Benchmarking

• Interpretation of oil, gas, and water production trends.
• Analysis of wellhead pressure, bottomhole pressure, temperature, and fluid-level measurements.
• Evaluation of productivity index, pump efficiency, drawdown, and production decline.
• Application of production KPIs, performance benchmarking, and trend analysis.
• Identification of measurement errors, missing data, and inconsistencies in production reporting.
• Case Study: Analyze historical production and pressure data from a group of producing wells to distinguish reservoir-related decline from artificial lift performance deterioration.

Module 4: Electrical Submersible Pump (ESP) Surveillance

• Monitoring motor current, voltage, frequency, temperature, and vibration indicators.
• Analysis of pump intake pressure, discharge pressure, and differential pressure.
• Identification of gas locking, pump-off conditions, overheating, and abnormal loading.
• Evaluation of operating frequency, pump performance curves, and production response.
• Application of ESP failure diagnostics and equipment run-life analysis.
• Case Study: Investigate an ESP well experiencing declining production and rising motor current. 

Module 5: Gas Lift Surveillance and Injection Performance

• Fundamentals of continuous and intermittent gas lift operations.
• Monitoring gas injection rates, casing pressure, tubing pressure, and production response.
• Evaluation of gas lift valve performance and injection gas distribution.
• Identification of under-injection, over-injection, unstable flow, and gas allocation inefficiencies.
• Optimization of injection gas allocation across multiple producing wells.
• Case Study: Analyze a gas lift field with constrained compression capacity and recommend an injection gas redistribution strategy to improve overall production performance.

Module 6: Rod Pumping, PCP, and Alternative Lift Surveillance

• Monitoring rod pump stroke rate, polished rod load, torque, and fluid levels.
• Interpretation of dynamometer cards and pump fillage conditions.
• Diagnosis of gas interference, fluid pound, rod loading, and mechanical inefficiencies.
• Surveillance of PCP torque, speed, pressure differential, and equipment condition.
• Comparison of alternative lift technologies using reliability and production performance indicators.
• Case Study: Review dynamometer cards and production trends from a rod-pumped well experiencing fluctuating output. 

Module 7: Digital Surveillance, Predictive Analytics, and Automation

• Integration of SCADA, production historians, and automated well monitoring platforms.
• Application of data visualization, surveillance dashboards, and automated exception reporting.
• Introduction to anomaly detection and predictive maintenance analytics.
• Use of machine learning concepts for identifying abnormal operating patterns.
• Application of digital twins and condition monitoring to artificial lift performance management.
• Case Study: Design a digital surveillance dashboard that flags abnormal ESP current, unexpected production decline, and deviations from established operating limits across a producing asset.

Module 8: Failure Analysis, Optimization, and Performance Improvement

• Application of root cause analysis and structured equipment failure investigation.
• Assessment of equipment reliability, mean time between failures, and run-life trends.
• Development of corrective and preventive action plans for artificial lift systems.
• Evaluation of energy consumption, operating expenditure, and production deferment.
• Preparation of performance improvement recommendations and continuous surveillance plans.
• Case Study: Investigate repeated artificial lift failures in a mature oilfield. 

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.