Advanced Reservoir Simulation Formulation, Initial of Boundary Condition and Running the Simulator
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Advanced Reservoir Simulation Formulation, Initial of Boundary Condition and Running the Simulator Course
Introduction:
Dynamic reservoir models are important when investigating reservoir behaviour, optimizing reservoir performance, designing complex wells, estimating uncertainties and providing the basis for risk management. New developments, such as unstructured gridding, combined with new simulation techniques eliminate most of the drawbacks of conventional simulation methods and make predictions more reliable. The participants will learn about various algorithms, concepts and possible uses of reservoir simulators.
Course Objectives:
Upon successful completion of this course, the delegates will be able to:
- Apply the principles of reservoir engineering to numerical modelling
- Set up, run, and analyze 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
Who Should Attend?
This course is intended for all experienced reservoir engineers. Attendees should have a basic knowledge of reservoir simulation, stochastic modeling, upscaling and some experience in the use of commercial reservoir simulators. Reservoir engineers, with a few years of practical experience. Petroleum engineers that require more than general knowledge of reservoir engineering.
Course Outlines:
Modeling concepts
- The concept of grid blocks and time steps
- Consequences of discretization
- Explicit and implicit functions
- Treatment of vertical saturation and pressure distributions
- History matching
- Well management
- Solution methods
- Review of fluid properties for simulation - black-oil properties, equation of state Modelling
Rock properties and saturation functions - porosity, permeability, compressibility, relative permeability, capillary pressure, compaction, correlations
Designing the reservoir model
- Checklist for model design
- Selecting the number of dimensions
- Simplification of complex problems
- Representation of reservoir fluids
- Representation of reservoir rock
- Well models – coupling between well and reservoir
- Selecting Reservoir – Rock and Fluid Properties Data
- Data required for model construction
- The sensitivity of results to data accuracy
- Porosity and permeability
- Assignment of rock property distributions to the simulator
- Capillary pressure and relative permeability
- Fluid properties
- Establishing initial pressure and saturation distributions
Selecting grid and time step sizes
- Criteria for selecting grid block size
- Selection of grid block size
- Example grids
- Selection of time steps
- Limiting numerical dispersion
- Grid orientation
- Cost consideration
Selecting the numerical solution method
- Terminology
- Formulating the equations
- Formulation options
- Numerical dispersion
- Choosing the formulation option
- Matrix equations
- Solution methods
- Selecting the equation-solving technique
Well management: designing and controlling production parameters
- The overall design of a well-management routine
- Logic structure
- Logic sequence
- Individual well behavior
- Operations conditions
- Initial of boundary conditions and running the simulator
- Data requirement
- Up gridding and upscaling
- General-purpose formulation and discretization methods used for black-oil and EOS
- Compositional simulators
- Gridding - structured and unstructured gridding approaches, Cartesian grids, corner point grids, Voronoi grids
Modelling structural elements in simulation - vertical and sloping faults, channels, etc…
History matching
- Objectives of matching historical reservoir performance
- Strategy and plans for history matching
- Manual adjustment of history- matching parameters
- Examples of adjustment required in history matching
- Special considerations in history matching
- Automatic history matching
Compositional reservoir simulation
Forecasting future performance
- Planning the prediction cases to be run
- Preparation of input data for predictions
- Making a smooth transition from history to predictions
- Review and analysis of predicted performance
- Evaluating and monitoring predicted performance
Simulating special processes
- Compositional simulation
- Miscible displacement