Development Optimization and Uncertainty Analysis Methods for Oil and Gas Reservoirs

Amin Ettehadtavakkol, Christopher Jablonowski, Larry Lake

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Uncertainty complicates the development optimization of oil and gas exploration and production projects, but methods have been devised to analyze uncertainty and its impact on optimal decision-making. This paper compares two methods for development optimization and uncertainty analysis: Monte Carlo (MC) simulation and stochastic programming. Two example problems for a gas field development and an oilfield development are solved and discussed to elaborate the advantages and disadvantages of each method. Development optimization involves decisions regarding the configuration of initial capital investment and subsequent operational decisions. Uncertainty analysis involves the quantification of the impact of uncertain parameters on the optimum design concept. The gas field development problem is designed to highlight the differences in the implementation of the two methods and to show that both methods yield the exact same optimum design. The results show that both MC optimization and stochastic programming provide unique benefits, and that the choice of method depends on the goal of the analysis. While the MC method generates more useful information, along with the optimum design configuration, the stochastic programming method is more computationally efficient in determining the optimal solution. Reservoirs comprise multiple compartments and layers with multiphase flow of oil, water, and gas. We present a workflow for development optimization under uncertainty for these reservoirs, and solve an example on the design optimization of a multicompartment, multilayer oilfield development.

Original languageEnglish
Pages (from-to)177-190
Number of pages14
JournalNatural Resources Research
Issue number2
StatePublished - Apr 1 2017


  • Oil and gas field development optimization
  • Optimization with Monte Carlo sampling
  • Stochastic programming


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