Advanced Reservoir Engineering Training Course

1. Advanced Reservoir Characterization

1.1 Geological Modeling

• Advanced static modeling workflows including (structural modeling, facies modeling, and property modeling)

• Integration of seismic data including (seismic attributes, inversion results, and geostatistical relationships)

• Multi-scale heterogeneity characterization including (core-scale, well-scale, inter-well scale, and field-scale integration)

• Advanced geostatistical methods including (multiple-point statistics, object-based modeling, and machine learning approaches)

• Uncertainty handling in geological models including (multiple realizations, probability maps, and ranking methodologies)

• Fracture network modeling including (discrete fracture networks, dual porosity concepts, and geomechanical constraints)

1.2 Petrophysical Evaluation

• Advanced core analysis including (special core analysis, digital rock physics, and pore network modeling)

• Multi-mineral log interpretation including (deterministic approaches, probabilistic methods, and machine learning techniques)

• NMR interpretation including (T1-T2 mapping, permeability estimation, and fluid typing)

• Saturation height modeling including (capillary pressure integration, J-functions, and free water level concepts)

• Integration of formation testing data including (pressure gradients, fluid sampling, and mobility measurements)

1.3 Fluid Characterization

• Advanced PVT analysis including (equation of state modeling, compositional gradient studies, and phase behavior prediction)

• Equation of state tuning including (regression methods, pseudoization techniques, and validation approaches)

• Near-critical fluid behavior including (phase transitions, retrograde condensation, and critical point determination)

• Laboratory data integration including (DL/DE studies, separator tests, and swelling tests)

• Fluid sampling challenges including (representativeness, contamination assessment, and sample validation)

• Heavy oil characterization including (viscosity modeling, asphaltene precipitation, and thermal properties)

2. Advanced Reservoir Engineering Analysis

2.1 Well Test Analysis

• Pressure transient analysis for complex reservoirs including (naturally fractured systems, multi-layered reservoirs, and heterogeneous formations)

• Deconvolution techniques including (pressure-rate deconvolution, superposition principles, and boundary identification)

• Interference testing including (multi-well analysis, tomography approaches, and reservoir connectivity assessment)

• Production data analysis including (rate transient analysis, flowing material balance, and decline curve analysis)

• Integration of dynamic data including (production logging, downhole pressure gauges, and distributed temperature sensing)

• Horizontal and multi-fractured well testing including (linear flow regimes, fracture characterization, and effective permeability determination)

2.2 Material Balance Methods

• Generalized material balance equations including (drive mechanism quantification, aquifer modeling, and energy balance)

• Advanced aquifer modeling including (analytical models, numerical aquifers, and history matching techniques)

• Gas and gas condensate reservoirs including (real gas pseudopressure, retrograde condensation effects, and gas cycling)

• Compartmentalized reservoirs including (pressure communication, baffles identification, and fluid contacts movement)

• Integration with simulation models including (tank model calibration, simulation initialization, and history matching constraints)

2.3 Dynamic Reservoir Surveillance

• Production monitoring techniques including (flow rate measurements, water cut tracking, and GOR evolution)

• 4D seismic interpretation including (time-lapse analysis, saturation mapping, and pressure effects)

• Real-time data acquisition including (permanent downhole gauges, fiber optic systems, and data quality control)

• Tracer technology applications including (interwell tracers, single-well tracers, and sweep efficiency determination)

• Integrated surveillance programs including (data acquisition plans, monitoring frequency, and key performance indicators)

3. Reservoir Simulation

3.1 Advanced Simulation Techniques

• Simulation model construction including (upscaling, property population, and initial conditions setting)

• History matching workflows including (sensitivity analysis, parameterization techniques, and assisted history matching)

• Uncertainty quantification including (experimental design, response surface modeling, and Monte Carlo simulation)

• Production optimization including (well placement optimization, development scenario evaluation, and facility constraints)

• Advanced numerical methods including (discretization schemes, solver algorithms, and convergence enhancement)

• High-performance computing including (parallel processing, GPU utilization, and cloud-based simulation)

3.2 Specialized Simulation Applications

• Fractured reservoir simulation including (dual porosity/dual permeability models, discrete fracture networks, and embedded discrete fracture models)

• Compositional simulation including (phase behavior representation, component tracking, and miscible processes)

• Thermal simulation including (heat transfer mechanisms, viscosity reduction modeling, and steam processes)

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

• Coupled geomechanical simulation including (compaction, subsidence, and stress-dependent permeability)

• CO2 sequestration modeling including (trapping mechanisms, long-term migration, and reactive transport)

3.3 Next-Generation Simulation

• Data-driven modeling including (proxy models, reduced order modeling, and machine learning approaches)

• AI applications including (deep learning, reinforcement learning, and neural networks for reservoir characterization)

• Cloud-based simulation including (web applications, remote visualization, and collaborative environments)

• Integration with real-time data including (model updating, ensemble-based methods, and continuous calibration)

• Digital twin concepts including (real-time simulation, predictive analytics, and decision support systems)

4. Enhanced Oil Recovery

4.1 Thermal Methods

• Steam injection processes including (cyclic steam stimulation, steamflooding, and SAGD)

• In-situ combustion including (reaction kinetics, combustion front propagation, and operational challenges)

• Hot water flooding including (temperature effects on mobility, sweep efficiency, and heat management)

• Hybrid thermal approaches including (solvent-assisted processes, steam-solvent combinations, and electromagnetic heating)

• Field implementation considerations including (well completion requirements, surface facilities, and monitoring techniques)

4.2 Chemical Methods

• Polymer flooding including (polymer types, rheology, retention mechanisms, and injectivity considerations)

• Surfactant flooding including (phase behavior, interfacial tension reduction, and adsorption effects)

• Alkaline flooding including (in-situ soap generation, pH effects, and mineral reactions)

• Combined chemical processes including (ASP formulation, synergistic effects, and stability considerations)

• Field-scale implementation including (chemical handling, injection strategies, and performance monitoring)

• Specialty chemicals including (foam agents, conformance control agents, and low-salinity effects)

4.3 Gas Injection Methods

• Miscible gas injection including (minimum miscibility pressure, compositional effects, and mixing zone behavior)

• Immiscible gas injection including (gravity drainage, mobility control, and incremental recovery mechanisms)

• WAG processes including (cycle optimization, conformance control, and operational considerations)

• CO2 EOR including (CO2 properties, storage potential, and combined recovery/sequestration approaches)

• Novel gas processes including (foam-assisted gas injection, gas-chemical combinations, and huff-and-puff applications)

4.4 Novel and Emerging EOR Technologies

• Low-salinity waterflooding including (wettability alteration mechanisms, ion interactions, and screening criteria)

• Microbial EOR including (microorganism selection, nutrient requirements, and metabolic products)

• Nanoparticle applications including (stability enhancement, interfacial effects, and transport mechanisms)

• Smart water including (ion tuning, water composition optimization, and rock-fluid interactions)

• Electromagnetic techniques including (radio frequency heating, electrical resistance heating, and inductive heating)

5. Unconventional Reservoir Engineering

5.1 Shale Reservoir Characterization

• Organic-rich shale properties including (total organic content, thermal maturity, and kerogen typing)

• Pore system characterization including (organic porosity, inorganic porosity, and multiscale pore networks)

• Gas storage mechanisms including (free gas, adsorbed gas, and dissolved gas)

• Fluid phase behavior in nanopores including (confinement effects, critical property shifts, and non-Darcy flow)

• Geochemical characterization including (rock-eval pyrolysis, mineralogy assessment, and brittleness evaluation)

5.2 Hydraulic Fracture Engineering

• Fracture design optimization including (pump schedule, proppant selection, and fluid formulation)

• Fracture diagnostics including (microseismic monitoring, fiber optics, and pressure analysis)

• Multi-stage completion strategies including (plug-and-perf, sliding sleeves, and limited entry perforating)

• Frac hits mitigation including (well spacing optimization, fracture sequencing, and pressure management)

• Parent-child well relationships including (depletion effects, stress shadowing, and infill drilling strategies)

• Re-fracturing candidate selection including (production analysis, remaining potential, and execution strategies)

5.3 Production Analysis for Unconventional Reservoirs

• Rate transient analysis including (linear flow regimes, fracture interference, and SRV estimation)

• Decline curve analysis including (modified hyperbolic decline, stretched exponential decline, and Duong method)

• Advanced production forecasting including (statistical methods, type curves, and physics-based models)

• Pressure and rate normalized methods including (flowing material balance, normalized productivity index, and pressure-rate deconvolution)

• Production optimization including (artificial lift selection, liquid loading mitigation, and choke management)

6. Integrated Reservoir Management

6.1 Field Development Planning

• Optimization of development strategies including (well placement, drilling sequence, and facility sizing)

• Decision analysis including (decision trees, value of information, and flexible development concepts)

• Scenario-based planning including (high-side, low-side, and base case scenarios)

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

• Resource progression including (resource classification, reserves booking criteria, and field maturation)

• Brownfield redevelopment including (infill drilling opportunities, intervention campaigns, and late-life strategies)

6.2 Uncertainty and Risk Management

• Uncertainty quantification techniques including (experimental design, response surface modeling, and Monte Carlo simulation)

• Probabilistic reserves estimation including (deterministic vs. probabilistic approaches, aggregation methods, and dependencies handling)

• Decision making under uncertainty including (expected value concepts, risk profiles, and utility theory)

• Value of information analysis including (data acquisition justification, uncertainty reduction potential, and optimal learning strategies)

• Portfolio management including (project ranking, resource allocation, and optimization under constraints)

6.3 Digital Transformation in Reservoir Management

• Big data analytics including (data mining, pattern recognition, and anomaly detection)

• Machine learning applications including (supervised learning, unsupervised learning, and reinforcement learning)

• Real-time optimization including (closed-loop reservoir management, adaptive control, and automated workflows)

• Digital oilfield implementation including (instrumentation, data integration, and visualization platforms)

• Data management strategies including (data quality control, database architecture, and collaborative environments)

7. Reserves Estimation and Economic Analysis

7.1 Reserves Classification and Reporting

• Reserves definitions including (SPE-PRMS guidelines, SEC regulations, and other international standards)

• Classification criteria including (technical certainty, commercial certainty, and project status)

• Documentation requirements including (technical reports, audit trails, and uncertainty assessments)

• Reserves aggregation including (arithmetic summation, statistical aggregation, and dependency considerations)

• Reserves reconciliation including (production reconciliation, technical revisions, and acquisitions/divestitures)

7.2 Economic Analysis

• Cash flow modeling including (revenue forecasting, operating expenses, capital expenditures, and fiscal terms)

• Economic indicators including (NPV, IRR, profitability index, and payback period)

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

• Price forecasting including (market analysis, cycle effects, and scenario planning)

• Project screening including (economic yardsticks, ranking methodologies, and portfolio optimization)

• Decision analysis including (expected monetary value, value of flexibility, and real options valuation)

8. HSE in Reservoir Engineering

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

• Carbon capture and storage including (site selection, monitoring requirements, and long-term containment)

• Produced water management including (treatment options, reinjection considerations, and disposal alternatives)

• Risk assessment methodologies including (hazard identification, consequence analysis, and mitigation planning)

• Regulatory compliance including (reporting requirements, permitting processes, and abandonment provisions)

9. Case Studies & Group Discussions

• Regional case studies from Middle East operations including (carbonate reservoirs, heterogeneous formations, and heavy oil deposits)

• Integrated field development examples including (full-field studies, optimization projects, and EOR implementations)

• Production enhancement success stories including (before/after comparison, key success factors, and lessons learned)

• Recovery factor improvement cases including (reservoir management strategies, technology applications, and economic outcomes)

• The importance of proper training in successful reservoir management practices