Integrated Asset Modeling for Petroleum Operations Training Course

Integrated Asset Modeling for Petroleum Operations Training Course

Introduction

Integrated Asset Modeling for Petroleum Operations Training Course is designed to equip petroleum engineers, reservoir engineers, production specialists, and oil and gas asset managers with advanced skills in integrated asset modeling, reservoir simulation, production optimization, and field development planning. Integrated asset modeling (IAM) is a powerful approach that combines reservoir performance, well behavior, surface facilities, production networks, and economic evaluation into a unified digital representation of petroleum assets. This course explores digital oilfield technologies, predictive analytics, dynamic simulation, production forecasting, reservoir management, and real-time asset surveillance to help participants understand the complex interactions between reservoir conditions, well productivity, gathering systems, processing facilities, and export infrastructure. Participants will develop the technical capabilities needed to improve operational efficiency, enhance recovery factors, reduce operating costs, and maximize the value of oil and gas investments throughout the asset lifecycle.

Participants will learn how to construct and calibrate integrated asset models, connect reservoir simulation models with production network models, evaluate alternative operating strategies, and forecast asset performance under changing technical and economic conditions. Through industry-based case studies, interactive exercises, and scenario analysis, learners will investigate challenges such as declining reservoir pressure, water and gas breakthrough, pipeline capacity constraints, artificial lift optimization, and facility processing limitations. The program also examines how machine learning, real-time production data, automated history matching, and advanced decision-support systems can improve model reliability and accelerate operational planning. By the end of the training, participants will be better prepared to support integrated field development, optimize production systems, evaluate capital investments, and implement sustainable, cost-effective strategies that maximize recovery and profitability across upstream oil and gas assets.

Course Duration

5 days

Course Objectives

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

1. Explain the principles, architecture, and strategic benefits of integrated asset modeling (IAM) in petroleum operations.
2. Develop integrated models linking reservoir simulation, well performance, production networks, and surface facilities.
3. Apply reservoir-to-facility optimization techniques to improve total asset productivity.
4. Evaluate reservoir behavior using pressure, fluid properties, production history, and recovery mechanisms.
5. Analyze well deliverability using inflow performance relationships and vertical lift performance.
6. Apply multiphase flow simulation to identify flow restrictions and optimize production networks.
7. Integrate reservoir, well, pipeline, and processing facility models into a consistent asset-wide workflow.
8. Perform production forecasting and scenario-based simulation to evaluate future asset performance.
9. Use history matching and model calibration to improve integrated model accuracy and reliability.
10. Identify production bottlenecks and recommend targeted operational interventions.
11. Apply artificial lift optimization and well allocation strategies to maximize hydrocarbon production.
12. Conduct uncertainty analysis, sensitivity testing, and economic evaluation to support investment decisions.
13. Develop practical strategies for implementing digital oilfield solutions, predictive analytics, and digital twin technologies in petroleum asset management.

Target Audience

1. Reservoir engineers and reservoir simulation specialists.
2. Petroleum engineers and production engineering professionals.
3. Well performance and artificial lift engineers.
4. Facilities, process, and surface network engineers.
5. Integrated asset managers and field development planners.
6. Production technologists and petroleum operations supervisors.
7. Digital oilfield, data analytics, and petroleum modeling specialists.
8. Technical consultants, project engineers, and upstream investment analysts.

Course Modules

Module 1: Fundamentals of Integrated Asset Modeling

• Definition, scope, and business value of integrated asset modeling.
• Core components of an integrated petroleum asset model.
• Relationships between reservoirs, wells, pipelines, and processing facilities.
• Data requirements, model architecture, and integration workflows.
• Case study: Evaluating how reservoir pressure decline affects well production and surface facility utilization.

Module 2: Reservoir Simulation and Subsurface Modeling

• Reservoir characterization, geological models, and fluid distribution.
• Reservoir pressure, permeability, porosity, and relative permeability.
• Black-oil and compositional reservoir simulation concepts.
• Production history analysis, reservoir surveillance, and material balance.
• Case study: Integrating a reservoir simulation model to forecast production from a mature oilfield with declining pressure.

Module 3: Well Performance and Production Modeling

• Inflow performance relationships and well productivity analysis.
• Vertical lift performance and pressure-drop calculations.
• Well completion design and production constraint evaluation.
• Artificial lift systems, including gas lift and electric submersible pumps.
• Case study: Optimizing gas lift injection rates across a group of wells to increase total field production.

Module 4: Production Network and Multiphase Flow Modeling

• Modeling gathering networks, flowlines, manifolds, and pipelines.
• Multiphase flow behavior and pressure-loss calculations.
• Network hydraulics, backpressure effects, and flow allocation.
• Pipeline capacity management and production constraint identification.
• Case study: Resolving a production bottleneck caused by excessive backpressure in a shared gathering network.

Module 5: Surface Facilities and Processing Integration

• Modeling separators, compressors, pumps, and processing equipment.
• Gas processing, water handling, and oil stabilization constraints.
• Facility capacity assessment and production system debottlenecking.
• Integrating operating limits into asset-wide production models.
• Case study: Evaluating the impact of separator capacity limitations on well production and overall field output.

Module 6: Integrated Model Calibration, History Matching, and Forecasting

• Data quality assessment and model consistency checks.
• Reservoir and production network model calibration.
• Integrated history matching and reconciliation of production data.
• Short-term and long-term production forecasting.
• Case study: Calibrating an integrated model against historical production to improve forecasts for a field experiencing unexpected water breakthrough.

Module 7: Production Optimization, Uncertainty, and Economic Evaluation

• Integrated production optimization and well allocation strategies.
• Sensitivity analysis for reservoir, well, and facility parameters.
• Uncertainty quantification and probabilistic production forecasting.
• Capital expenditure, operating expenditure, and economic performance analysis.
• Case study: Comparing workover, artificial lift, and facility upgrade options to determine the most economically attractive intervention plan.

Module 8: Digital Oilfield, Digital Twins, and Advanced Asset Management

• Real-time production surveillance and data integration.
• Digital twin concepts and continuous model updating.
• Machine learning applications in production forecasting and anomaly detection.
• Automated workflows, decision-support systems, and model governance.
• Case study: Designing a digital asset monitoring workflow that combines live well data, production network models, and facility constraints to support daily optimization decisions.

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.