Order diminution (OD) or model order reduction (MOR), a very important field of System Engineering, has been explored by many researchers. Different methods are available for reducing the complexity of a control system, which are subsequently utilized to get a cost-effective controller. Model order reduction is done by either using classical methods or by using optimization techniques. In optimization algorithms, accuracy, complexity, and convergent rate are the main criteria for comparison in OD. This paper contributes a novel fast and more accurate OD (MOR) technique based on Salp Swarm Optimization. Further, the proposed method is applied to a time-delay system in four different manners. The effectiveness of the proposed technique is shown by reducing four benchmark systems, including a system with time delay and an 84th order system. Finally, the application of OD is shown by designing a reduced-based H-infinity controller for the 84th order system which results in a great saving of time (% 96%). The obtained results are comparable or better than those from the existing well-known order reduction techniques available in the literature.

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Asset managers constantly seek to determine how wells are performing to assess performance of long-term strategy and to achieve expected results. For this purpose, periodic production tests are performed to measure individual flow contribution to total measured platform production in addition to other measurements, including BSW, gas-oil ratio, pressure, and temperature. Daily well production is estimated by back-allocating production measured at fiscal meters to individual wells based on the well’s production potential validated during tests. This paper presents an alternative system for measuring individual well-oil production based on a neural network and online correlation logic using data from sensors, well tests, and simulations. This system permits a closer right-time monitoring of the wells by enabling readings to be taken more frequently and by minimizing the intrinsic estimation errors that normally arise when doing back-allocation of well production based on performance of other wells. This paper describes a methodology for data selection, sensor validation analysis, modeling, online implementation, and quality control of the results. The main benefit of this implementation has been to quickly identify production deviations above or below well potential and to identify and adjust the variables that affect these deviations. The combination of high volumes of measured data that automation technology enables and historical values of testing data made it possible to implement this smart solution where data is constantly transformed into information. This information allows the engineers to analyze and associate results and transform them into events of knowledge. This methodology can be applied to any asset where time and operational constraints do not permit the testing of wells on a daily basis or where it is too expensive to justify the installation of multiphase meters and where a high level of automation is available.