Well control optimization using derivative-free algorithms and a multiscale approach. (6th April 2019)

• Record Type: Journal Article
• Authors: Wang, Xiang; Haynes, Ronald D.; He, Yanfeng; Feng, Qihong
• Is Part Of: Computers & chemical engineering. Volume 123(2019)
• Page Start: 12
• Page End: 33
• Publication Date: 2019-04-06
• Subjects: Well control -- Production optimization -- Derivative-free algorithms -- Multiscale approach; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2018.12.004
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: GPS, PSO, and CMA-ES are combined with a multiscale framework. Settings for GPS, PSO, and CMA-ES, within the multiscale framework are considered. CMA-ES performs best among the three algorithms without the multiscale framework. GPS improving the most when hybridized with the multiscale framework. The effect of two key parameters of the multiscale framework are investigated. Abstract: Smart well technologies, which allow remote control of well and production processes, make the problem of determining optimal control strategies a timely and valuable pursuit. The large number of well rates for each control step make the optimization problem difficult and present a high risk of achieving a suboptimal solution. Moreover, the optimal number of adjustments is not known a priori. Adjusting well controls too frequently will increase unnecessary well management and operation cost, and an excessively low number of control adjustments may not be enough to obtain a good yield. In this paper, we explore the capability of three derivative-free algorithms and a multiscale regularization framework for well control optimization over the life of an oil reservoir. The derivative-free algorithms chosen include generalized pattern search (GPS), particle swarm optimization (PSO) and covariance matrix adaptation evolution strategy (CMA-ES). These algorithms, which cover a variety of search strategies (global/local search, stochastic/deterministic search), are chosen due to their robustness and easy parallelization. Although these algorithms have been used extensively in the reservoir development optimization literature, for the first time we thoroughly explore how these algorithms perform when hybridized within a multiscale regularization framework. Starting with a reasonably small number of control steps, the control intervals are subsequently refined during the optimization. Results for the experiments studied indicate that CMA-ES performs best among the three algorithms in solving both small and large scale problems. When hybridized with a multiscale regularization approach, the ability to find the optimal solution is further enhanced, with the performance of GPS improving the most. Topics affecting the performance of the multiscale approach are discussed in this paper, including the effect of control frequency on the well control problem. The parameter settings for GPS, PSO, and CMA-ES, within the multiscale approach, are considered.