A robust, multi-solution framework for well location and control optimization

Salehian, M. and Sefat, M. Haghighat and Muradov, K.; (2020) A robust, multi-solution framework for well location and control optimization. In: ECMOR 2020 - 17th European Conference on the Mathematics of Oil Recovery. ECMOR 2020 - 17th European Conference on the Mathematics of Oil Recovery . European Association of Geoscientists and Engineers, EAGE, Virtual, Online, pp. 1-16. ISBN 9789462823426 (https://doi.org/10.3997/2214-4609.202035113)

[thumbnail of Salehian-etal-ECMOR-2020-A-robust-multi-solution-framework-for-well-location-and-control-optimization]
Text. Filename: Salehian_etal_ECMOR_2020_A_robust_multi_solution_framework_for_well_location_and_control_optimization.pdf
Accepted Author Manuscript
License: Strathprints license 1.0

Download (2MB)| Preview


Optimal field development and control aim to maximize the economic profit of oil and gas production while considering various constraints. This results in a high-dimensional optimization problem with a computationally demanding and uncertain objective function based on the simulated reservoir models. The limitations of many current robust optimization methods are: 1) they optimize only a single level of control variables (e.g. well locations only; or well production/injection scheduling only) that ignores the interferences between control variables from different levels; and 2) they provide a single optimal solution, whereas operational problems often add unexpected constraints that result in adjustments to this optimal solution scenario degrading its value. This paper presents a robust, multi-solution framework based on sequential iterative optimization of control variables at multiple levels. Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm is used as the optimizer while the estimated gradients are calculated using a 1:1 ratio mapping ensemble of control variables perturbations at each iteration onto the ensemble of selected reservoir model realizations. An ensemble of closeto- optimum solutions is then chosen from each level (e.g. from the well placement optimization level) and transferred to the next level of optimization (e.g. where the control settings are optimized), and this loop continues until no significant improvement is observed in the expected objective value. Fit-for-purpose clustering techniques are developed to systematically select an ensemble of realizations to capture the underlying model uncertainties, as well as an ensemble of solutions with sufficient differences in control variables but close-to-optimum objective values, at each optimization level. The proposed framework has been tested on the Brugge benchmark field case study. Multiple solutions are obtained with different well locations and control settings but close-to-optimum objective values, providing the much-needed operational flexibility to field operators. We also show that suboptimal solutions from an early optimization level can approach and even outdo the optimal one at the next level(s) demonstrating the advantage of the developed framework in a more efficient exploration of the search space.