Reservoir Simulation

BACKGROUND

Production and operation teams cannot directly observe how conditions change within a reservoir during production. Instead, they depend on sophisticated simulation models that transform subsurface. Therefore, reservoir simulation serves as a cornerstone for understanding reservoir performance and optimizing recovery strategies. Integrating geoscience, production data, engineers can forecast future performance under various scenarios, identify operational bottlenecks, and design effective recovery schemes. Advances in gridding techniques, rock/fluid characterization, and model calibration have further elevated the accuracy and reliability of these predictions.

In recent years, the rise of data-driven modeling, machine learning, and advanced analytics has transformed how engineers approach simulation. These tools complement physics-based models by uncovering hidden patterns, enhancing predictive capabilities, and improving optimization in areas.  Those areas can be fracture design, water/gas injection management, and predictive maintenance. It will equip participants with practical skills and integrated workflows to maximize reservoir performance in today’s competitive energy landscape.

OBJECTIVES:

This Course will discuss the following topics:

• Review of:
  – Buckley Leverett displacement
  – One dimensional water oil displacement
  – Model components, types, and modern gridding methods
  – Two-dimensional displacement
  – Grid orientation and refinement
  – Routine and special core analysis
  – Single phase up-scaling of geo-cellular model parameters
• Apply the principles of reservoir engineering to numerical modelling
• Set up, run, and analyse the results for single-well, pattern, and full-field models
• Prepare fluid and rock property data in the manner required for simulation studies
• Identify and eliminate causes of numerical problems
• Perform a history match
• Use the matched model to predict future performance under a variety of assumptions
• Introduction to advanced analytical tools and techniques that includes data mining and machine learning algorithms along with means to access them easily over open-source platforms – Python and Google’s Tensor Flow.
• Application of each of these tools to specific subsurface data and the successful implementation leading to optimization/decision making.
•  Sweet spots/new acreage identification as well as likely optimum frac stages for unconventional production using existing reservoir data as well as public data.
•  Application of artificial neural networks for a) predictive maintenance on surface facilities, b) identifying lithology by formation evaluation data and c) fluid characterization
• Optimize water and/or gas injection operations in conventional fields by application of these advanced tools on production data collected as part of surveillance
• Framework to integrate these advanced modelling tools with existing workflows such as reservoir simulation using case study to explain the same.

• Recommended Participants
• Course Outline
• About The Course Leader

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