Advanced Production Surveillance Training Course

Advanced Production Surveillance Training Course

Introduction

Advanced Production Surveillance Training Course is designed to equip oil and gas professionals with advanced technical knowledge and practical skills for monitoring, evaluating, and optimizing hydrocarbon production performance. In an increasingly data-driven energy industry, effective production surveillance is essential for maximizing reservoir productivity, improving well performance, reducing operating costs, and sustaining long-term asset value. This course explores advanced production monitoring techniques, real-time production data analytics, well performance diagnostics, reservoir surveillance, production allocation, decline curve analysis, and integrated asset management. Participants will learn how to interpret production trends, identify abnormal well behavior, detect production losses, evaluate artificial lift performance, and implement proactive interventions using modern digital oilfield technologies. Through the application of predictive analytics, intelligent production monitoring, automated surveillance systems, and data-driven decision-making, professionals will strengthen their ability to identify production bottlenecks and translate operational data into measurable business outcomes.

The course also emphasizes the integration of multidisciplinary data, including pressure and temperature measurements, flow rates, water cut, gas-oil ratio, well test results, reservoir pressure, and equipment operating parameters. Participants will develop practical competencies in production diagnostics, well integrity monitoring, production optimization workflows, anomaly detection, and performance benchmarking across individual wells and entire fields. Real-world case studies, practical exercises, and simulated operational scenarios will demonstrate how production surveillance supports early identification of declining well productivity, water or gas breakthrough, artificial lift inefficiencies, flow assurance challenges, and equipment failures. The program also examines emerging technologies such as AI-powered production surveillance, machine learning for anomaly detection, digital twins, IoT-enabled sensors, cloud-based production dashboards, and integrated operations centers. By the end of the training, participants will be better prepared to establish effective surveillance strategies, improve production reliability, accelerate troubleshooting, and support sustainable hydrocarbon recovery through continuous performance monitoring and operational excellence.

Course Duration

10 days

Course Objectives

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

1. Develop advanced production surveillance strategies aligned with reservoir management objectives and field development plans.
2. Interpret real-time production data to identify performance deviations, operational inefficiencies, and emerging production risks.
3. Apply advanced well performance diagnostics to detect productivity decline, abnormal flow behavior, and production constraints.
4. Utilize production data analytics and visualization tools to generate actionable operational insights.
5. Evaluate well test results, production allocation methods, and measurement uncertainty for reliable production accounting.
6. Implement reservoir surveillance techniques to monitor pressure depletion, fluid movement, and hydrocarbon recovery performance.
7. Apply predictive analytics and machine learning concepts to detect production anomalies and anticipate equipment failures.
8. Diagnose artificial lift performance issues involving ESPs, gas lift, rod pumps, and other lift systems.
9. Assess water cut, gas-oil ratio, and fluid composition trends to identify breakthrough and changing reservoir conditions.
10. Establish production key performance indicators (KPIs) and performance benchmarking frameworks for wells and assets.
11. Integrate well integrity, flow assurance, and equipment monitoring into comprehensive surveillance workflows.
12. Design digital production dashboards and automated alerts to improve operational visibility and response times.
13. Formulate continuous production optimization plans that improve recovery, reduce downtime, and maximize asset profitability.

Target Audience

1. Production engineers
2. Reservoir engineers
3. Petroleum engineers 
4. Well intervention and well services engineers 
5. Artificial lift specialists managing ESP, gas lift, and rod pump performance.
6. Production technologists and operations engineers 
7. Data analysts and digital oilfield specialists
8. Asset managers, field supervisors, and technical team leaders

Course Modules

Module 1: Fundamentals of Advanced Production Surveillance

• Principles and objectives of production surveillance in upstream oil and gas operations.
• Relationship between production surveillance, reservoir management, and field development planning.
• Key surveillance parameters: oil rate, gas rate, water cut, pressure, and temperature.
• Establishing surveillance frequency, workflows, responsibilities, and escalation procedures.
• Integrating surveillance findings into production optimization decisions.
• Case study: Establishing a production surveillance framework for a mature oilfield experiencing declining output and inconsistent well performance.

Module 2: Production Data Acquisition and Quality Management

• Sources of production data from well tests, SCADA systems, sensors, and metering equipment.
• Production data validation, reconciliation, and quality assurance techniques.
• Identification of missing values, measurement errors, and inconsistent time-series records.
• Data standardization, well identification, and production database management.
• Assessing measurement uncertainty and its impact on surveillance decisions.
• Case study: Investigating conflicting production reports caused by faulty meters, inconsistent well tests, and incomplete operational records.

Module 3: Advanced Well Performance Diagnostics

• Evaluating well productivity using production rates, flowing pressures, and productivity indices.
• Identifying abnormal production behavior through trend analysis and diagnostic plots.
• Applying nodal analysis concepts to identify wellbore and surface-system restrictions.
• Differentiating reservoir-related decline from mechanical and operational problems.
• Developing structured root-cause analysis and well performance troubleshooting workflows.
• Case study: Diagnosing a sudden production decline in an oil producer and determining whether the cause is reservoir depletion, formation damage, or surface backpressure.

Module 4: Reservoir Surveillance and Pressure Monitoring

• Monitoring reservoir pressure changes and pressure depletion patterns.
• Interpreting pressure transient data and well test information for surveillance purposes.
• Tracking reservoir connectivity, fluid movement, and changing production behavior.
• Integrating pressure measurements with geological and reservoir simulation models.
• Assessing pressure support, injection effectiveness, and recovery performance.
• Case study: Evaluating pressure decline across a producing field to identify underperforming reservoir compartments and opportunities for pressure support.

Module 5: Production Trend Analysis and Decline Diagnostics

• Applying production trend analysis to identify short-term and long-term performance changes.
• Using decline curve analysis to evaluate production behavior and estimate future performance.
• Identifying abnormal decline rates and distinguishing operational events from natural depletion.
• Evaluating cumulative production, recovery trends, and remaining production potential.
• Developing well-level and field-level production forecasts using historical data.
• Case study: Analyzing historical production trends from a mature field to distinguish natural decline from avoidable operational losses.

Module 6: Water Cut, Gas-Oil Ratio, and Fluid Surveillance

• Monitoring water cut trends and identifying unexpected increases in water production.
• Evaluating gas-oil ratio changes and their implications for reservoir and well performance.
• Detecting potential water breakthrough, gas breakthrough, and changing fluid contacts.
• Assessing coning, channeling, and water or gas production mechanisms.
• Developing surveillance-based recommendations for water control and production management.
• Case study: Investigating a rapidly increasing water cut in an oil producer and recommending additional diagnostics and potential water-control interventions.

Module 7: Artificial Lift Surveillance and Optimization

• Monitoring ESP operating parameters, including intake pressure, discharge pressure, current, and vibration.
• Evaluating gas lift performance using injection rates, pressure conditions, and production response.
• Diagnosing rod pump inefficiencies, fluid pound, and pump fillage problems.
• Identifying artificial lift failures, unstable operation, and equipment degradation.
• Integrating surveillance findings with lift optimization and intervention planning.
• Case study: Diagnosing declining output from an ESP-equipped well using electrical parameters, pressure trends, and production data to determine the appropriate corrective action.

Module 8: Production Allocation and Measurement Reconciliation

• Principles of well-level production allocation and field production accounting.
• Evaluating well test quality, test frequency, and representativeness.
• Reconciling well-level measurements with separator, manifold, and export measurements.
• Identifying allocation discrepancies, meter drift, and unaccounted production losses.
• Establishing measurement governance and auditable production reporting workflows.
• Case study: Resolving a discrepancy between allocated well production and the measured field export volume through systematic measurement reconciliation.

Module 9: Flow Assurance and Production Constraint Surveillance

• Monitoring production system pressure drops and temperature profiles.
• Identifying potential scale, wax, hydrate, and asphaltene-related restrictions.
• Evaluating erosion, corrosion, and changing flow conditions using operating data.
• Diagnosing wellhead, flowline, separator, and gathering-system constraints.
• Integrating flow assurance surveillance with production engineering and maintenance planning.
• Case study: Investigating progressive production losses in a flowline system and evaluating whether increasing pressure drop indicates deposition or another restriction.

Module 10: Well Integrity and Equipment Health Monitoring

• Monitoring annulus pressure, wellhead pressure, and other relevant integrity indicators.
• Identifying abnormal pressure behavior and potential integrity concerns.
• Integrating equipment operating data with maintenance and inspection records.
• Applying condition-based monitoring and reliability-centered maintenance concepts.
• Establishing risk-based escalation procedures for integrity-related surveillance alerts.
• Case study: Reviewing an unexplained annulus pressure increase and developing a risk-based investigation and escalation plan.

Module 11: Production Surveillance Using Digital Oilfield Technologies

• Integrating SCADA, historians, production databases, and operational information systems.
• Using real-time dashboards to monitor production performance and operational constraints.
• Configuring automated alerts for abnormal rates, pressure deviations, and equipment anomalies.
• Applying IoT-enabled sensors and remote monitoring to improve field visibility.
• Evaluating digital twins and integrated operations centers for production decision support.
• Case study: Designing a digital surveillance dashboard for a multiwell asset with geographically dispersed production facilities.

Module 12: AI, Machine Learning, and Predictive Production Analytics

• Understanding applications of artificial intelligence in production surveillance.
• Using anomaly detection methods to identify deviations from expected well behavior.
• Applying machine learning concepts to production forecasting and performance classification.
• Evaluating predictive maintenance models for artificial lift and production equipment.
• Assessing data quality, model reliability, false alarms, and human oversight requirements.
• Case study: Developing a conceptual anomaly detection workflow to identify wells likely to experience production underperformance based on historical operating data.

Module 13: Production Surveillance KPIs and Performance Benchmarking

• Defining production KPIs for well productivity, uptime, deferment, and operating efficiency.
• Calculating production availability and assessing the impact of unplanned downtime.
• Benchmarking well performance against expected production and comparable wells.
• Developing surveillance scorecards and exception-based reporting systems.
• Linking technical indicators to production targets, operating costs, and asset value.
• Case study: Comparing performance across a group of producing wells to prioritize the wells with the greatest potential for production recovery.

Module 14: Integrated Surveillance, Deferment Analysis, and Intervention Planning

• Quantifying production deferment caused by well, equipment, and facility constraints.
• Integrating reservoir, production, artificial lift, and maintenance information.
• Prioritizing surveillance exceptions according to production impact, risk, and intervention cost.
• Developing corrective action plans with assigned responsibilities and measurable outcomes.
• Tracking intervention effectiveness through before-and-after production performance analysis.
• Case study: Ranking a portfolio of underperforming wells and selecting interventions based on recoverable production, operational risk, cost, and expected value.

Module 15: Integrated Field Surveillance Strategy and Capstone Project

• Designing an integrated surveillance program for a producing oil or gas asset.
• Establishing data governance, monitoring frequency, and exception management workflows.
• Integrating production forecasting, reservoir surveillance, and equipment performance monitoring.
• Preparing technical surveillance reports and communicating recommendations to management.
• Developing continuous improvement plans for production reliability and hydrocarbon recovery.
• Case study: Participants analyze a simulated field dataset containing production declines, pressure anomalies, rising water cut, and artificial lift issues.

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.