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A new Optimization Approach for Solving Well Placement Problem under Uncertainty Assessment

Darabi, Hamed | 2009

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 39142 (06)
  4. University: Sharif University of Technology
  5. Department: Chemical and Petroleum Engineering
  6. Advisor(s): Massihi, Mohsen; Roosta Azad, Reza
  7. Abstract:
  8. Determining the best location for new wells is a complex problem that depends on reservoir and fluid properties, well and surface equipment specifications, and economic criteria. Various approaches have been proposed for this problem. Among those, direct optimization using the simulator as the evaluation function, although accurate, is in most cases infeasible due to the number of simulations required. This study proposes a hybrid optimization technique (HGA) based on the genetic algorithm (GA) with helper functions based on the polytope algorithm and the neural network. Hybridization of the GA with these helper methods introduces hill-climbing into the stochastic search and also makes use of proxies created and calibrated iteratively throughout the run, following the idea of using cheap substitutes for the expensive numerical simulation. Also, In the case of well placement optimization problem, the constructed numerical model relies on data that is incomplete, thus the numerical simulation forecasts are uncertain. Therefore, a deterministic global solution is not available in the presence of the uncertainty. In this work, Fuzzy Inference system (FIS) is used to address this problem. The output of the FIS determines the goodness of a point that is selected for drilling a well, by integrating the effects of uncertainty, engineering sense and the decision maker preferences. As it is used in various economical and engineering applications, Net Present Value (NPV) is a common objective function in well placement problems. In this work, the FIS output is incorporated into NPV, and a new objective function (corrected NPV or CNPV) is constructed for the well placement optimization problems.
    Finally, Performance of the technique was investigated by optimizing placement of water injection wells in a section of real field in western part of Iran. It was observed from controlled experiments that the number of simulations required to find optimal well configurations was reduced significantly by using HGA. Also, Well configurations and injection rates, up to four wells, were optimized with corrected net present value maximization of the waterflooding project as the objective.
  9. Keywords:
  10. Optimization ; Genetic Algorithm ; Artificial Neural Network ; Decision Making ; Fuzzy Inference System ; Uncertainty Analysis ; Well Placement ; Polytope Method

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