Workshop III: Data Assimilation, Uncertainty Reduction, and Optimization for Subsurface Flow

May 22 - 26, 2017

Overview

OILWS3 graphicThere are major computational challenges and substantial uncertainties associated with model inversion and performance optimization in oil field applications. As production and (in some cases) 4D seismic and electromagnetic data are collected, there are inevitably discrepancies between the predicted and actual reservoir responses. This drives the need for data assimilation, usually referred to as history matching in petroleum engineering. History matching involves the solution of a computationally demanding, highly ill-conditioned, inverse problem. Key complications that arise are the uncertain nature of the geology (and thus the need to “properly” sample the posterior distribution), combined with the need to retain geological realism in history-matched reservoir descriptions. Additional complexity enters as a result of the unknown rock-physics quantities needed to integrate multiphysics data sets. Computational optimization is also of great importance for oil and gas production, given the complexity of the reservoir flow response and the very high costs associated with large-scale field development. An emerging area is the joint optimization of field development and operation, in which decision variables could include the sequence and type (producer or injector, vertical or horizontal) of wells to be drilled, well locations, and time-varying controls. This problem involves real, integer and categorical variables and is thus a MINLP problem. Additional issues that arise are the need to perform these optimizations under uncertainty (i.e., robustly), and the need to treat multiple, possibly conflicting, objective functions.

This workshop is expected to include discussion of computational procedures addressing the following topics, among others:

  • Algorithms for inverse modeling/history matching, including use of production and geophysical data
  • Uncertainty quantification/reduction in flow predictions and seismic and electromagnetic inversion
  • Optimal design of reservoir surveillance programs
  • Optimization of oil and gas field development and operation under uncertainty
  • Optimization of unconventional (e.g., shale gas) resource plays and CO2 storage projects
  • Multi-objective optimization for oil field problems
  • Related/enabling topics such as reduced-order and proxy/surrogate modeling, proxy-based optimization/inversion, multilevel optimization, etc.
  • Approaches in other application areas such as hydrology/hydrogeology, basin modeling, weather prediction, etc.

This workshop will include a poster session; a request for poster titles will be sent to registered participants in advance of the workshop.

Organizing Committee

Lou Durlofsky, Chair (Stanford University)
Eldad Haber, Co-chair (University of British Columbia)
Jan-Dirk Jansen, Co-chair (Technische Universiteit te Delft)
Al Reynolds (University of Tulsa)
Xiao-Hui Wu (ExxonMobil)