Integration of multiphase flowmetering, neural networks, and fuzzy logic in field performance monitoring

Alimonti, C. and Falcone, G. (2004) Integration of multiphase flowmetering, neural networks, and fuzzy logic in field performance monitoring. SPE Production and Facilities, 19(1), pp. 25-32. (doi:10.2118/87629-PA)

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The usual approach to the interpretation of producing wells is based on mechanistic models for the simulation of steady-state and transient flow regimes. However, there are significant reservations about convergence problems, computational limits, the need for extensive tuning on field data, the instability of boundary conditions, the limited applicability of existing multiphase flow models, and the uncertainties associated with choke-valve models. The current industry standards are critically reviewed within this framework. The real-time monitoring of producing wells is recognized as the best way of optimizing field performance. Monitoring a producing well implies the ability to track any changes in fluid composition, flow rates, or pressure and temperature profiles in real time. Multiphase flowmetering (MFM) plays a key role in this scenario. Such information, combined with the critical analysis of historical data from the well itself or from analog wells, allows diagnosis of the system and prediction of future trends. However, field data per se do not necessarily generate knowledge. This is particularly true for large databases, which are difficult to manipulate to provide suitable inputs for wellbore simulators. This paper suggests how MFM, knowledge discovery in databases (KDD), and fuzzy logic (FL) can offer an alternative approach to analyzing producing wells. KDD is the automated extraction of patterns representing knowledge implicitly stored in large information repositories. Distributed, ad hoc field measurements (including MFM and downhole measurements) can be processed by means of data cleaning, data integration, data mining, artificial intelligence (AI), and pattern evaluation. FL can then manage the resulting information in terms of flow assurance and production optimization. The same techniques can also be extended to the reservoir and production network for an integrated approach to production-system analysis.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Falcone, Professor Gioia
Authors: Alimonti, C., and Falcone, G.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:SPE Production and Facilities
Publisher:Society of Petroleum Engineers

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