Well & Reservoir Surveillance Training Course

1. Introduction to Well and Reservoir Surveillance

1.1 Surveillance Fundamentals

• Purpose and objectives of surveillance including (asset optimization and production enhancement)

• Surveillance program components including (data acquisition, analysis, and implementation)

• Value of surveillance including (production optimization, recovery improvement, and cost reduction)

• Industry standards and guidelines including (SPE recommended practices and API RP 76)

• Surveillance in asset lifecycle including (exploration, development, production, and abandonment phases)

1.2 Surveillance Strategy Development

• Risk-based approach including (critical well identification and surveillance prioritization)

• Data requirements including (measurement parameters, frequency, and accuracy)

• Technology selection including (monitoring tools, data transmission, and storage systems)

• Resource allocation including (personnel requirements, equipment needs, and budget considerations)

• Performance metrics including (KPIs, benchmarking, and success criteria)

2. Data Acquisition and Management

2.1 Surveillance Data Types

• Well data including (pressures, temperatures, rates, and fluid properties)

• Reservoir data including (saturation, pressure, temperature, and fluid distribution)

• Production data including (rates, ratios, and compositions)

• Facilities data including (operational parameters, equipment performance, and integrity metrics)

• Environmental data including (regulatory compliance parameters and impact indicators)

2.2 Measurement Systems

• Surface measurement including (wellhead sensors, multiphase flowmeters, and test separators)

• Downhole measurement including (permanent gauges, distributed sensing, and memory tools)

• Fluid sampling including (surface sampling, downhole sampling, and PVT analysis)

• Reservoir monitoring including (4D seismic, tracer studies, and interference testing)

• Specialized measurements including (fiber optic sensing, acoustic monitoring, and resistivity arrays)

2.3 Data Management Systems

• Data architecture including (database design, integration platforms, and accessibility)

• Quality assurance including (validation protocols, uncertainty assessment, and error identification)

• Data processing including (filtering techniques, normalization methods, and reconciliation)

• Visualization tools including (dashboards, trends, and interactive displays)

• Data integration including (model inputs, cross-discipline sharing, and decision support systems)

3. Well Performance Monitoring

3.1 Production Logging

• Production log types including (flowmeters, temperature surveys, and hold-up tools)

• Measurement principles including (spinner physics, temperature effects, and phase behavior)

• Survey design including (tool selection, measurement program, and operational considerations)

• Data interpretation including (flow profile analysis, contribution evaluation, and problem identification)

• Advanced techniques including (array tools, fiber optic DTS/DAS, and interpretive methods)

3.2 Pressure Monitoring

• Bottom-hole pressure measurement including (permanent gauges, memory gauges, and surface calculation)

• Pressure transient data including (build-up tests, fall-off tests, and interference testing)

• Continuous pressure monitoring including (real-time surveillance, trend analysis, and alarm systems)

• Gradient surveys including (fluid gradient determination, fluid contact identification, and crossflow detection)

• Multi-point pressure monitoring including (intelligent completions, distributed sensing, and zonal isolation verification)

3.3 Production Testing

• Well test objectives including (rate determination, performance evaluation, and problem diagnosis)

• Test separator operations including (three-phase separation, measurement accuracy, and operational procedures)

• Multiphase flow measurement including (technology types, calibration methods, and uncertainty analysis)

• Test design including (duration determination, stabilization criteria, and data quality objectives)

• Test interpretation including (rate analysis, fluid properties evaluation, and performance assessment)

3.4 Artificial Lift Surveillance

• Rod pump monitoring including (dynamometer analysis, pump efficiency, and failure prediction)

• ESP surveillance including (electrical parameters, performance curves, and operating point optimization)

• Gas lift monitoring including (injection profiling, valve performance, and optimization techniques)

• Other lift systems including (jet pump monitoring, hydraulic pump surveillance, and progressive cavity pump analysis)

• Integrated lift optimization including (system efficiency, energy consumption, and run life extension)

4. Reservoir Surveillance

4.1 Reservoir Pressure Monitoring

• Pressure survey design including (well selection, frequency determination, and methodology)

• Reservoir pressure mapping including (isobar generation, depletion tracking, and compartmentalization analysis)

• Interference testing including (design principles, execution methods, and connectivity analysis)

• Pressure maintenance evaluation including (injection effectiveness, support mechanisms, and optimization)

• Depletion monitoring including (drive mechanism evaluation, energy assessment, and recovery projection)

4.2 Fluid Contact Monitoring

• Contact monitoring methods including (logging techniques, pressure gradient analysis, and production data)

• Water movement tracking including (aquifer influx, flood front progression, and breakthrough prediction)

• Gas cap expansion including (gas-oil contact movement, cap volume estimation, and coning evaluation)

• Saturation monitoring including (resistivity analysis, neutron methods, and production chemistry)

• Advanced techniques including (4D seismic interpretation, cross-well tomography, and tracer studies)

4.3 Injection Monitoring

• Injection profiles including (coverage evaluation, conformance assessment, and sweep efficiency)

• Injectivity analysis including (skin monitoring, fracture evolution, and capacity trends)

• Pattern performance including (volumetric balance, efficiency metrics, and pattern redesign)

• Breakthrough monitoring including (tracer studies, chemical fingerprinting, and production response)

• Advanced techniques including (chemical tracer analysis, temperature logs, and pressure pulse testing)

4.4 Formation Evaluation for Surveillance

• Time-lapse logging including (saturation monitoring, formation damage assessment, and contact movement)

• Cased-hole techniques including (pulsed neutron, carbon/oxygen logs, and resistivity measurements)

• Core analysis including (special core analysis, sponge coring, and core preservation)

• Formation sampling including (fluid gradient analysis, compositional tracking, and PVT updates)

• Integrated evaluation including (multi-physics interpretation, model calibration, and uncertainty reduction)

5. Production System Surveillance

5.1 Well Integrity Monitoring

• Barrier verification including (pressure testing, leak detection, and valve functionality)

• Corrosion monitoring including (calipers, electromagnetic tools, and ultrasonic techniques)

• Scale monitoring including (production chemistry, deposition prediction, and treatment effectiveness)

• Sand production including (detection methods, quantification techniques, and control evaluation)

• Well integrity management system including (data integration, risk assessment, and intervention planning)

5.2 Flow Assurance Surveillance

• Hydrate monitoring including (temperature profiling, pressure conditions, and inhibitor effectiveness)

• Wax deposition including (temperature modeling, deposition prediction, and treatment optimization)

• Asphaltene management including (onset prediction, deposition monitoring, and treatment effectiveness)

• Scale tendency including (water analysis, scaling indices, and inhibitor performance)

• Emulsion behavior including (water cut effects, demulsifier optimization, and separation efficiency)

5.3 Network and Facilities Surveillance

• Gathering system monitoring including (pressure profiles, flow distribution, and bottleneck identification)

• Processing equipment including (separator performance, compression efficiency, and treatment system monitoring)

• Backpressure effects including (system interactions, wellhead pressure optimization, and artificial lift implications)

• Energy efficiency including (power consumption, fuel gas usage, and optimization opportunities)

• System capacity including (constraint analysis, debottlenecking opportunities, and expansion planning)

6. Data Analysis and Interpretation

6.1 Production Analysis Techniques

• Decline curve analysis including (decline type identification, rate projection, and reserves estimation)

• Nodal analysis including (system modeling, constraint identification, and optimization scenarios)

• Rate transient analysis including (flow regime identification, reservoir characterization, and boundary effects)

• Water-oil ratio analysis including (trend interpretation, breakthrough timing, and channeling diagnosis)

• Gas-oil ratio analysis including (solution gas behavior, gas cap influence, and phase behavior)

6.2 Pattern and Trend Analysis

• Production trending including (rate behavior, ratio changes, and anomaly detection)

• Surveillance frequency analysis including (sampling theory, statistical significance, and detection limits)

• Statistical methods including (multivariate analysis, data clustering, and correlation techniques)

• Analog analysis including (similar well comparison, pattern matching, and predictive methods)

• Visualization techniques including (bubble maps, spider diagrams, and time-based animations)

6.3 Integrated Analysis Methods

• Multi-disciplinary interpretation including (geology, reservoir engineering, and production engineering integration)

• Model calibration including (history matching, performance reconciliation, and predictive refinement)

• Uncertainty analysis including (sensitivity studies, probabilistic assessment, and confidence levels)

• Recovery factor analysis including (volumetric reconciliation, efficiency metrics, and improvement opportunities)

• Optimization workflow including (integrated assessment, scenario modeling, and decision support)

7. Surveillance for Reservoir Management

7.1 Surveillance for Primary Recovery

• Depletion monitoring including (pressure decline patterns, drive mechanism confirmation, and recovery efficiency)

• Compartmentalization analysis including (pressure discontinuities, fluid contact variations, and production behavior)

• Natural water drive monitoring including (aquifer strength, support mechanisms, and sweep patterns)

• Solution gas drive optimization including (GOR management, critical rate determination, and gas handling)

• Primary recovery enhancement including (infill opportunities, artificial lift optimization, and completion modifications)

7.2 Surveillance for Secondary Recovery

• Waterflood monitoring including (injection distribution, sweep efficiency, and pattern performance)

• Gas injection monitoring including (conformance control, breakthrough timing, and recycling optimization)

• IOR technique evaluation including (mobility control, wettability modification, and displacement efficiency)

• Pattern optimization including (surveillance-based redesign, injection rate allocation, and well conversion timing)

• Recovery efficiency evaluation including (volumetric verification, displacement efficiency, and sweep improvement)

7.3 Surveillance for Enhanced Recovery

• Chemical EOR monitoring including (chemical tracer analysis, injectivity trends, and response indicators)

• Thermal recovery surveillance including (temperature profiling, steam quality, and heated zone mapping)

• Miscible gas monitoring including (miscibility pressure verification, conformance control, and breakthrough management)

• Novel techniques including (foam injection monitoring, microbial activity, and low salinity effects)

• Pilot test surveillance including (pattern isolation, parameter control, and scale-up evaluation)

8. Problem Diagnosis and Optimization

8.1 Production Problem Diagnosis

• Production decline analysis including (mechanical vs. reservoir causes and diagnostic approaches)

• Water production problems including (coning, channeling, and behind-casing leaks)

• Gas production issues including (gas cap breach, gas coning, and tubing restrictions)

• Formation damage evaluation including (skin analysis, permeability reduction mechanisms, and remediation options)

• Artificial lift problems including (equipment failures, inefficiency causes, and operational issues)

8.2 Well Performance Optimization

• Drawdown optimization including (critical rate determination, sand management, and coning control)

• Artificial lift optimization including (operating point adjustment, efficiency improvement, and system redesign)

• Completion optimization including (perforation enhancement, remedial operations, and recompletion opportunities)

• Stimulation opportunity identification including (candidate selection, treatment design, and effectiveness verification)

• Workover candidate selection including (ranking methodologies, economic screening, and intervention planning)

8.3 Field-wide Optimization

• Production allocation including (well prioritization, capacity distribution, and constraint management)

• Back-allocation including (gathering system modeling, well contribution assessment, and uncertainty reduction)

• Integrated asset modeling including (reservoir-well-surface integration, constraint analysis, and scenario evaluation)

• Optimization workflows including (opportunity identification, solution development, and implementation planning)

• Value assessment including (economic analysis, risk evaluation, and investment prioritization)

9. Digital Surveillance Technologies

9.1 Real-time Monitoring Systems

• SCADA systems including (data acquisition architecture, communication protocols, and system integration)

• Remote monitoring including (telemetry, transmission security, and data compression)

• Real-time operations centers including (visualization systems, collaboration tools, and decision workflows)

• Alarm management including (threshold setting, escalation procedures, and response protocols)

• Edge computing including (local processing, data filtering, and bandwidth optimization)

9.2 Advanced Analytics

• Data mining including (pattern recognition, anomaly detection, and correlation discovery)

• Machine learning applications including (production forecasting, failure prediction, and optimization algorithms)

• Digital twins including (integrated modeling, real-time calibration, and predictive capabilities)

• Artificial intelligence including (advisory systems, natural language processing, and automated diagnosis)

• Advanced visualization including (augmented reality, 3D representation, and interactive dashboards)

9.3 Data Analytics Implementation

• Analytics workflow development including (use case identification, solution design, and implementation planning)

• Model development including (data preparation, algorithm selection, and validation techniques)

• Analytics infrastructure including (computing requirements, data storage, and processing architecture)

• Change management including (user adoption, workflow integration, and training requirements)

• Value measurement including (benefit tracking, performance metrics, and continuous improvement)

10. Surveillance Program Management

10.1 Surveillance Program Design

• Program scope including (asset coverage, surveillance objectives, and priority setting)

• Implementation strategy including (phased approach, technology roadmap, and resource allocation)

• Standards and protocols including (measurement standards, data quality requirements, and operating procedures)

• Personnel development including (competency requirements, training programs, and knowledge management)

• Governance structure including (roles and responsibilities, decision authority, and review processes)

10.2 Economic Evaluation

• Cost-benefit analysis including (program economics, value assessment, and justification metrics)

• Technology evaluation including (capital investment, operational costs, and value delivery)

• Resource optimization including (personnel utilization, equipment allocation, and budgeting)

• Value of information including (decision impact assessment, uncertainty reduction, and risk mitigation)

• Performance measurement including (KPI tracking, benefit realization, and program adjustment)

10.3 Program Implementation and Sustainability

• Implementation planning including (activity scheduling, resource management, and milestone tracking)

• Quality assurance including (data validation, calibration protocols, and performance verification)

• Continuous improvement including (feedback mechanisms, technology updates, and process refinement)

• Knowledge management including (lesson documentation, best practice sharing, and expert systems)

• Stakeholder engagement including (reporting mechanisms, communication strategies, and collaboration tools)

11. Case Studies & Group Discussions

• Regional case studies from Middle East operations including (carbonate reservoirs, layered formations, and challenging environments)

• Surveillance program success stories including (significant production increases, recovery enhancement, and cost reduction)

• Problem-solving exercises including (surveillance data interpretation, diagnosis workflows, and optimization planning)

• Integrated surveillance applications including (multidisciplinary approaches, data integration, and decision optimization)

• The importance of proper training in successful surveillance operations