PETROLEUM PRODUCTION ENGINEERING – II

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http://riohanggliding.com/?view=categories The increase of upstream activities both in current and new reservoirs, in increasingly challenging work locations, demand to work more efficiently, avoiding risk to people, capital, and environment. Therefore, monitoring the production operations has been emphasized creating large volumes of production data which is necessary to store, explore, and analyze. Management and use of this big data is critical for the oil industry. Production data analysis from different perspectives provides a platform for converting data it into information and knowledge. Integration of production data into the practice of petroleum production engineers is essential to establishing a vision for the oil and gas industry to move toward data-based decisions in the production and operations arena. This course will provide an overview of the advance production engineering methods by exploiting the sue of available data and data driven technologies actions to optimize asset production.

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Buy Diazepam Tablets This course is designed for professional process engineers, facility engineers, operation engineers, and asset managers

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  • Evaluate the performance of a hydrocarbon producing system by analyzing various data sources
  • Recognize production data patterns to unveil of petroleum production performance problems and remediating solutions
  • Discriminate general steps in the life cycle of production data from sensor generated data to usability
  • Optimize asset production by integrating production improvement decisions, field operating constraints and economics models
  • Build integrated asset models incorporating performance of reservoir, wells and surface facilities

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  • Production Data management
    • Oilfield Instrument status management
    • Data filtering and conditioning
    • Data validation and reconciliation
    • Production volume back allocation
    • Well rate estimation (Indirect Virtual rate metering)
  • Data mining and production surveillance
    • Critical data and desirable data
    • Data mining methods for production data clustering
    • Data driven modeling for production performance modeling
    • Statistical analysis (mean, deviation, percentiles, frequency, etc)
    • Exception based surveillance
  • Integrated production modeling
    • Interaction from near wellbore to separator flow path for a single well
    • Reservoir, wells and facilities
  • Asset Production Optimization
    • Optimization formulation (objective function, decision and constraints)
    • Optimization engines and methods
    • Cost modeling and economic KPI management (NPV, RoR, $/bbl, $/bpd)
    • Opportunity prioritization process
    • Production improvement opportunity decision making