Derivative-free techniques for optimal development of conventional and unconventional reservoirs

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Nasir, Yusuf, M.S. in Engineering

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The cyclic nature of oil price and the incentive from an optimal field development strategy has made the need for the development and management of oil and gas field to be done in an optimal fashion in order to maximize the asset value, while satisfying the optimization constraints which can be in the form of production limits, water cut, or well spacing. The optimal development plan for an oil field is hinged on the optimal locations and production scheme of wells in the field. Computational optimization algorithms, coupled with a reservoir simulator, have become increasingly popular in determining the optimal locations of wells and the optimal controls to be imposed on them. These algorithms should be able to deal with highly non-linear objective functions, the absence of gradient information, and a limited reservoir simulation budget. In this work, we considered derivative-free and non-invasive techniques: Enhanced Success History-Based Adaptive Differential Evolution (ESHADE) strategy with linear population size reduction, which is a variant of L-SHADE (recognized as one of the state-of-the-art global stochastic optimizer for continuous variable), and a Mesh Adaptive Direct Search (MADS) local pattern search method. These two methods are hybridized to develop a hybrid framework (E-MADS) that combines the advantageous aspects of both methods in order to improve optimization efficiency. Applications of these algorithms to the joint optimization of well location and time-varying control problem, with bounds and nonlinear constraints, are presented in this work. We considered both conventional and unconventional reservoirs in this work. In unconventional reservoirs, we considered the placement and control of horizontal wells and their corresponding length, the number of fractures, and fracture spacing. ESHADE is shown to outperform traditional global optimization algorithms such as Particle Swarm Optimization (PSO) and a real-coded Genetic Algorithm (GA). The E-MADS hybrid is also shown to have a superior performance relative to the standalone ESHADE and MADS methods for the joint optimization problem. We also incorporated a proxy in the E-MADS algorithm and it was shown to improve the efficiency of the hybrid algorithm.


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