Reservoir Simulation Training Course

1. Introduction to Reservoir Simulation

1.1 Simulation Fundamentals

• Purpose and applications of reservoir simulation including (field development planning, reserves estimation, and production optimization)

• Historical development of simulation technology including (black oil models, compositional models, and thermal models)

• Reservoir simulation workflow including (data gathering, model construction, history matching, and prediction)

• Types of simulation models including (black oil, compositional, thermal, and dual porosity/permeability)

• Introduction to SPE guidelines for reservoir simulation and EAGE standards

• Simulation limitations and common pitfalls including (numerical artifacts, non-uniqueness, and resolution constraints)

1.2 Mathematical Foundations

• Conservation equations including (mass conservation, energy conservation, and momentum conservation)

• Flow equations including (Darcy's law, multiphase extensions, and non-Darcy flow)

• PVT relationships including (black oil correlations, equation of state, and K-values)

• Numerical methods including (finite difference, finite volume, and finite element approaches)

• Discretization techniques including (spatial discretization, temporal discretization, and stability criteria)

• Matrix solution methods including (direct solvers, iterative solvers, and preconditioning)

1.3 Reservoir Simulation Software

• Commercial simulator overview including (capabilities, limitations, and selection criteria)

• Graphical user interfaces including (pre-processing, post-processing, and visualization tools)

• Input data structure including (grid data, rock properties, fluid properties, and well data)

• Output data interpretation including (well responses, field responses, and 3D visualizations)

• Simulator performance optimization including (run-time options, convergence settings, and parallel processing)

2. Geological Model to Simulation Model

2.1 Geological Modeling Review

• Structural modeling including (fault representation, horizon modeling, and zonation)

• Facies modeling including (deterministic approaches, stochastic methods, and object-based modeling)

• Petrophysical property modeling including (porosity, permeability, and net-to-gross)

• Upscaling considerations including (scale dependence, heterogeneity impact, and property preservation)

• Multi-realization approaches including (capturing uncertainty, scenario selection, and ranking methodologies)

2.2 Grid Construction

• Grid types including (Cartesian, corner point, unstructured, and hybrid grids)

• Grid resolution considerations including (appropriate cell sizing, aspect ratios, and computational efficiency)

• Local grid refinement including (near-well refinement, fault representation, and feature resolution)

• Property assignment including (geostatistical realizations, trends, and heterogeneity representation)

• Grid quality control including (cell geometry, property distributions, and connectivity analysis)

• Special gridding techniques including (multi-segment wells, hydraulic fractures, and dual continuum representations)

2.3 Property Upscaling

• Porosity upscaling including (arithmetic averaging, volume-weighted methods, and flow-based techniques)

• Permeability upscaling including (arithmetic, geometric, harmonic, and flow-based methods)

• Relative permeability upscaling including (pseudo-functions, dynamic pseudo-functions, and steady-state methods)

• Transmissibility calculations including (harmonic averaging, fault transmissibility multipliers, and non-neighbor connections)

• Saturation functions scaling including (J-function, leverett scaling, and saturation endpoint adjustments)

• Upscaling quality control including (flow response validation, property histograms, and sectoral flow calibration)

3. Model Initialization and Equilibration

3.1 Fluid Model Implementation

• Black oil model setup including (Rs, Bo, viscosity tables, and separator conditions)

• Compositional model setup including (equation of state, component properties, and binary interaction parameters)

• PVT data quality control including (data consistency, validation against correlations, and smoothing techniques)

• Thermal model considerations including (temperature dependence, rock-fluid heat transfer, and energy balance)

• Special fluid modeling including (miscible processes, foam models, and polymer rheology)

3.2 Rock-Fluid Interaction

• Relative permeability modeling including (laboratory measurements, correlations, and endpoint scaling)

• Capillary pressure implementation including (drainage and imbibition curves, J-function scaling, and numerical considerations)

• Hysteresis modeling including (trapping functions, scanning curves, and WAG applications)

• Advanced rock-fluid phenomena including (low-salinity effects, rate-dependent behavior, and three-phase models)

• Rock compaction effects including (pore volume multipliers, permeability reduction, and geomechanical coupling)

3.3 Initial Conditions

• Equilibration methods including (datum depth specification, contact depths, and capillary-gravity equilibrium)

• Initialization quality control including (material balance check, fluid distributions, and pressure gradients)

• Initial saturation distribution including (transition zones, historical production effects, and irreducible saturations)

• Region concepts including (equilibration regions, PVT regions, and SCAL regions)

• Aquifer modeling including (analytical aquifers, numerical aquifers, and geometric considerations)

• Pre-production validation including (volumetrics comparison, hydrostatic test, and material balance initialization)

4. Well Modeling and Constraints

4.1 Well Representation

• Well trajectory modeling including (vertical wells, deviated wells, horizontal wells, and multilateral wells)

• Completion modeling including (perforation intervals, limited entry, and inflow control devices)

• Well index calculations including (Peaceman model, effective radius concepts, and non-Darcy effects)

• Advanced well concepts including (hydraulic fractures, thermal wells, and intelligent completions)

• Multi-segment well modeling including (flow path representation, pressure drop calculations, and complex completions)

4.2 Production and Injection Controls

• Well controls including (rate targets, pressure limits, and economic constraints)

• Group controls including (production allocation, gas lift optimization, and surface network effects)

• Field constraints including (facility limitations, injection availability, and voidage replacement)

• Control switching including (primary and secondary constraints, control hierarchies, and constraint relaxation)

• Surface network coupling including (integrated asset modeling, backpressure effects, and facility constraints)

• Advanced control strategies including (smart well operations, flood management, and optimizer linkage)

5. History Matching

5.1 History Matching Fundamentals

• Objectives and workflow including (data preparation, quality control, and matching strategy)

• Historical data types including (rates, pressures, saturations, and 4D seismic)

• Data quality assessment including (measurement uncertainty, frequency analysis, and outlier detection)

• Match quality metrics including (global error measures, local mismatch indicators, and visual inspection)

• History matching strategy including (from global to local, sequential approach, and assisted history matching)

• Non-uniqueness challenges including (plausibility checks, geological constraints, and ensemble approaches)

5.2 Manual History Matching

• Global parameter adjustments including (aquifer strength, regional permeability, and pore volume multipliers)

• Local parameter modifications including (permeability multipliers, transmissibility adjustments, and well parameters)

• Saturation match techniques including (relative permeability adjustment, capillary pressure tuning, and endpoint scaling)

• Pressure match techniques including (compressibility modifications, boundary condition adjustments, and volume calibration)

• Structured approach to history matching including (impact analysis, parameter sensitivity, and geological consistency)

5.3 Assisted and Automated History Matching

• Parameterization techniques including (zonation methods, pilot points, and grid property manipulation)

• Objective function definition including (weighted observations, normalization techniques, and penalty terms)

• Optimization algorithms including (gradient-based methods, direct search methods, and evolutionary algorithms)

• Ensemble methods including (ensemble Kalman filter, Monte Carlo simulation, and experimental design)

• Machine learning applications including (proxy modeling, pattern recognition, and automated workflow design)

• Multi-objective optimization including (Pareto concepts, trade-off analysis, and solution selection)

6. Prediction and Forecasting

6.1 Prediction Case Design

• Development scenario definition including (well placement, drilling schedule, and facility constraints)

• Production strategy optimization including (drawdown management, voidage replacement, and sweep efficiency)

• Enhanced recovery evaluation including (waterflooding, gas injection, chemical EOR, and thermal methods)

• Field development options including (platform locations, artificial lift, and surface network design)

• Facility constraints modeling including (processing capacity, export limitations, and injection availability)

• Economic limit considerations including (rate thresholds, water cut limits, and GOR constraints)

6.2 Uncertainty Analysis

• Uncertainty workflow including (parameter selection, probabilistic modeling, and response surface generation)

• Experimental design including (one-factor-at-a-time, factorial design, and Latin hypercube sampling)

• Monte Carlo simulation including (random sampling, stratified sampling, and importance sampling)

• Representative models selection including (P10-P50-P90 cases, scenario-based approach, and multiple realization management)

• Decision analysis including (expected value concepts, value of information, and risk assessment)

• Sensitivity analysis including (tornado charts, spider diagrams, and critical parameter identification)

6.3 Advanced Prediction Methods

• Production optimization including (well placement optimization, control optimization, and rate allocation)

• Closed-loop reservoir management including (real-time data assimilation, continuous model updating, and adaptive control)

• Proxy modeling including (response surface methodology, reduced order modeling, and decline curve integration)

• Machine learning applications including (production forecasting, anomaly detection, and pattern recognition)

• Integrated forecasting including reservoir-wellbore-surface network coupling, economic models, and decision support)

7. Special Applications in Reservoir Simulation

7.1 Unconventional Reservoir Simulation

• Shale reservoir modeling including (dual porosity concepts, adsorption phenomena, and geomechanical coupling)

• Hydraulic fracture representation including (explicit fracture modeling, tartan gridding, and embedded discrete fracture models)

• Multi-scale physics including (nano-scale flow, conventional Darcy flow, and fracture flow)

• Specialized flow mechanisms including (Knudsen diffusion, slippage effects, and stress-dependent properties)

• History matching challenges including (early data limitations, fracture uncertainty, and interference effects)

• Production forecasting including (decline curve integration, EUR estimation, and refracturing assessment)

7.2 Enhanced Oil Recovery Simulation

• Chemical flooding modeling including (polymer rheology, surfactant phase behavior, and alkaline reactions)

• Miscible gas injection including (compositional effects, minimum miscibility pressure, and dispersion modeling)

• Thermal simulation including (heat transport, phase behavior, and energy balance)

• WAG processes including (hysteresis effects, cycle optimization, and three-phase flow modeling)

• Novel EOR processes including (low salinity effects, nanoparticle applications, and microbial EOR)

• CO2 sequestration including (solubility trapping, mineral reactions, and long-term monitoring)

7.3 Fractured Reservoir Simulation

• Dual porosity/dual permeability concepts including (transfer functions, shape factors, and matrix-fracture exchange)

• Discrete fracture network integration including (upscaling methods, connectivity analysis, and property assignment)

• Embedded discrete fracture models including (unstructured gridding, non-neighbor connections, and transmissibility calculation)

• Geomechanical coupling including (stress-dependent permeability, fracture aperture changes, and fault reactivation)

• Naturally fractured carbonate modeling including (vug representation, super-k zones, and multi-scale heterogeneity)

8. Specialized Modeling Techniques

8.1 Coupled Modeling

• Geomechanical coupling including (stress-dependent properties, compaction, and subsidence)

• Surface network integration including (facility constraints, back-pressure effects, and network optimization)

• Streamline simulation including (flow visualization, allocation factors, and time-of-flight analysis)

• Integrated asset modeling including (reservoir-well-surface facility coupling, economic models, and optimization)

• Thermal-hydraulic-mechanical coupling including (temperature effects, stress evolution, and formation integrity)

8.2 Data Integration

• 4D seismic integration including (saturation maps, pressure maps, and joint inversion)

• Production logging incorporation including (flow profiles, phase distribution, and crossflow identification)

• Tracer data utilization including (breakthrough analysis, connectivity assessment, and swept volume estimation)

• Real-time data assimilation including (permanent downhole gauges, smart well data, and continuous updating)

• Multi-disciplinary data coordination including (collaborative workflows, consistency checks, and uncertainty propagation)

9. HSE in Reservoir Simulation Studies

• Environmental impact assessment including (water management, emissions prediction, and ecological footprint)

• Carbon capture and storage modeling including (trapping mechanisms, leakage risk assessment, and monitoring design)

• Produced water management including (water cut forecasting, reinjection planning, and disposal options)

• Risk assessment methodologies including (containment evaluation, facility integrity, and operational safety)

• Regulatory compliance including (reporting requirements, permitting documentation, and abandonment planning)

10. Case Studies & Group Discussions

• Regional case studies from Middle East operations including (carbonate reservoirs, naturally fractured formations, and heterogeneous systems)

• Simulation project execution examples including (workflow demonstration, quality control processes, and decision support)

• Challenging history match case studies including (complex reservoirs, limited data scenarios, and innovative solutions)

• Field development optimization including (scenario comparison, uncertainty handling, and economic analysis)

• The importance of proper training in successful simulation studies