Integrated Subsurface Asset Management Training Course

Integrated Subsurface Asset Management Training Course

Introduction

Integrated Subsurface Asset Management Training Course is designed to strengthen the technical, analytical, and strategic capabilities required to optimize petroleum assets throughout their lifecycle. The course focuses on integrating reservoir engineering, petroleum geoscience, petrophysics, production engineering, reservoir simulation, and field development planning to improve subsurface understanding and maximize hydrocarbon recovery. Participants will explore modern approaches to subsurface data integration, static and dynamic reservoir modeling, uncertainty quantification, production forecasting, reserves estimation, and asset performance evaluation. By connecting geological interpretations with engineering models and operational data, the training enables professionals to make informed, data-driven decisions that support reservoir optimization, production efficiency, recovery factor improvement, and long-term asset value creation.

The course also emphasizes the application of digital oilfield technologies, artificial intelligence (AI), machine learning, integrated asset modeling, real-time production surveillance, and predictive analytics in subsurface asset management. Participants will learn how to evaluate development scenarios, manage reservoir uncertainties, optimize well placement, prioritize infill drilling opportunities, and align subsurface strategies with commercial objectives, environmental responsibilities, and operational constraints. Through practical exercises, industry-based case studies, multidisciplinary workshops, and field development simulations, participants will develop the skills needed to manage complex petroleum assets more effectively. The course provides a practical framework for improving subsurface decision-making, capital allocation, production forecasting accuracy, reservoir management strategies, and economic recovery, while supporting sustainable and competitive oil and gas operations in an increasingly technology-driven energy industry.

Course Duration

5 days

Course Objectives

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

1. Integrate multidisciplinary subsurface data from geology, geophysics, petrophysics, reservoir engineering, and production operations.
2. Develop reliable static and dynamic reservoir models for improved subsurface characterization and asset planning.
3. Apply advanced reservoir management strategies to maximize hydrocarbon recovery and improve field performance.
4. Evaluate reservoir properties using geological interpretation, petrophysical analysis, and well log data integration.
5. Implement integrated asset modeling (IAM) to connect reservoir behavior, well performance, and surface production facilities.
6. Apply reservoir simulation and production forecasting techniques to evaluate development scenarios and future asset potential.
7. Use AI-driven analytics and digital oilfield technologies to strengthen subsurface surveillance and decision-making.
8. Perform uncertainty quantification and sensitivity analysis to assess geological, technical, and operational risks.
9. Optimize well placement, infill drilling, and workover opportunities using subsurface performance data.
10. Develop effective reserves estimation and resource evaluation strategies aligned with recognized industry frameworks.
11. Evaluate enhanced oil recovery (EOR) opportunities and identify suitable reservoir optimization technologies.
12. Integrate technical, economic, environmental, and operational considerations into field development planning.
13. Establish performance indicators and continuous improvement strategies to enhance asset value, production efficiency, and recovery economics.

Target Audience

1. Petroleum and reservoir engineers responsible for reservoir performance and production optimization.
2. Geologists and geophysicists involved in subsurface interpretation and reservoir characterization.
3. Petrophysicists working on formation evaluation, well logging, and rock-property analysis.
4. Production engineers and well-performance specialists responsible for hydrocarbon production.
5. Subsurface asset managers and petroleum asset team leaders overseeing integrated field performance.
6. Field development planners and reserves evaluation specialists involved in investment decisions.
7. Digital oilfield specialists, data scientists, and engineers applying AI and analytics to subsurface operations.
8. Oil and gas project managers, technical consultants, and decision-makers responsible for asset economics and lifecycle planning.

Course Modules

Module 1: Fundamentals of Integrated Subsurface Asset Management

• Principles, scope, and strategic importance of integrated subsurface asset management.
• Petroleum asset lifecycle management, from exploration to abandonment.
• Roles of geology, geophysics, petrophysics, reservoir engineering, and production teams.
• Multidisciplinary collaboration, subsurface governance, and technical decision frameworks.
• Case study: Integrating multidisciplinary teams to resolve declining production in a mature offshore oil field.

Module 2: Subsurface Data Integration and Reservoir Characterization

• Acquisition, validation, and integration of geological, geophysical, and engineering data.
• Seismic interpretation, structural mapping, and stratigraphic analysis.
• Petrophysical evaluation of porosity, permeability, water saturation, and net pay.
• Identification of reservoir heterogeneity, faults, fractures, and fluid contacts.
• Case study: Combining seismic, well log, core, and pressure data to improve reservoir connectivity understanding in a heterogeneous sandstone field.

Module 3: Static Reservoir Modeling and Geological Uncertainty

• Construction of structural frameworks and three-dimensional geological models.
• Facies modeling, property distribution, and reservoir compartmentalization.
• Integration of core analysis, well logs, and seismic-derived attributes.
• Modeling geological uncertainty through alternative realizations and sensitivity analysis.
• Case study: Comparing alternative geological models to assess the impact of fault connectivity on recoverable reserves.

Module 4: Dynamic Reservoir Modeling and Simulation

• Fundamentals of reservoir simulation and dynamic model initialization.
• History matching using pressure, production, and water-cut data.
• Forecasting reservoir performance under alternative operating conditions.
• Simulation of depletion, water injection, gas injection, and pressure-support strategies.
• Case study: History matching and forecasting a mature oil reservoir to identify the most effective pressure-maintenance strategy.

Module 5: Integrated Asset Modeling and Production Optimization

• Integration of reservoir, wellbore, gathering systems, and surface facilities.
• Nodal analysis, inflow performance, and vertical lift performance.
• Production bottleneck identification and well-performance optimization.
• Allocation of production targets across wells and facilities under operating constraints.
• Case study: Using integrated asset modeling to resolve network constraints and increase production from a multiwell offshore asset.

Module 6: Reservoir Management, Recovery Enhancement, and Well Planning

• Development of reservoir management plans and field surveillance strategies.
• Identification and ranking of infill drilling and workover opportunities.
• Waterflood optimization, injection management, and pressure-support evaluation.
• Screening enhanced oil recovery methods, including gas injection and chemical EOR.
• Case study: Ranking infill drilling locations and water-injection adjustments to improve recovery from a mature onshore field.

Module 7: Digital Subsurface Management, AI, and Predictive Analytics

• Digital oilfield architecture and integration of subsurface data platforms.
• Application of AI and machine learning to production forecasting and anomaly detection.
• Real-time surveillance using pressure, temperature, flow, and well-test data.
• Predictive analytics for water breakthrough, production decline, and operational risks.
• Case study: Applying machine learning to production histories and well surveillance data to identify underperforming wells and prioritize interventions.

Module 8: Asset Economics, Uncertainty Management, and Strategic Planning

• Reserves estimation, resource classification, and production profile evaluation.
• Economic modeling using capital expenditure (CAPEX), operating expenditure (OPEX), and discounted cash flow.
• Probabilistic forecasting, sensitivity analysis, and scenario-based risk assessment.
• Integration of environmental performance, emissions reduction, and asset lifecycle considerations.
• Case study: Comparing base-case, accelerated-development, and EOR investment scenarios to select the most economically attractive field development strategy under uncertainty.

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.