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Smart Proxy Modeling for Numerical Reservoir Simulations – Big Data Analytics in E&P

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Course Credit: 0.15 CEU, 1.5 PDH

Computational science, addressing numerical solution to complex multi-physic, non-linear, partial differential equations, is at the forefront of engineering problem solving and optimization. Numerical Reservoir simulation, the application of computational science in petroleum engineering, is computationally expensive. Proxy models (statistical response surfaces, or reduced physics) attempt to make it practical to use the simulation models for field development planning and uncertainty quantification by addressing their computational footprint (with limited success rate).

Data-Driven Smart proxies take advantage of the “Big Data” solutions (machine learning and pattern recognition) to develop highly accurate replicas of numerical models with very fast response time. The novelty of Smart Proxy Modeling stems from the fact that it is a complete departure from traditional approaches to modeling in the oil and gas industry and constitutes a major advancement in utilization and incorporation of Big Data solution in the E&P industry.

Instead of starting with first principle physics, smart proxies are models that are built based on observation of system behavior, through data, much like how human brain learns. Just imagine that a single run of a one-million grid block reservoir simulation model that includes 100 time-steps will generate 1,000,000 x 100 = 1×108 examples of pressure and saturation changes at the grid block level to learn from. Furthermore, only by making 10 simulation runs, the number of training examples will increase to a billion records. A large amount of information and knowledgeis embedded in this one billion example of how pressure and saturation in a reservoir changes as a function of initial and boundary conditions as well as a function of all other static and dynamic characteristics of the reservoir being modeled. Surrogate Reservoir Model (SRM) is the smart proxy of numerical reservoir simulation.

This web event includes:

Introduction to Big Data Analytics in E&P
Introduction to Numerical Reservoir Simulation
Description of Smart Proxy Model
Surrogate Reservoir Model (Smart Proxy of Reservoir Simulations)
Case Studies (Production optimization in Carbonates, CO2 Storage, History Matching)

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 1 chapter
  1 downloadable resource

Course Chapters

  • 1Smart Proxy Modeling for Numerical Reservoir Simulations – Big Data Analytics in E&P - Chapter 1
    Media Type: Video

Credits

Earn credits by completing this course0.15 CEU credit1.5 PDH credits

Speakers

Dr. Shahab Mohaghegh