Knowledge Discovery in Databases and Multiphase Flow Metering: The Integration of Statistics, Data Mining, Neural Networks, Fuzzy Logic, and ad hoc Flow Measurements Towards Well Monitoring and Diagnosis

Alimonti, C. and Falcone, G. (2002) Knowledge Discovery in Databases and Multiphase Flow Metering: The Integration of Statistics, Data Mining, Neural Networks, Fuzzy Logic, and ad hoc Flow Measurements Towards Well Monitoring and Diagnosis. In: SPE Annual Technical Conference and Exhibition, San Antonio, Texas, 29 Sept-2 Oct 2002, (doi:10.2118/77407-MS)

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Abstract

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 recognised as the best way of optimising field performance. Monitoring a producing well implies the ability to track, in real-time, any changes in fluid composition, flow rates, or pressure and temperature profiles. Multiphase Flow Metering (MFM) plays a key role in this scenario. Such information, combined with the critical analysis of historical data from the well itself or from analogue 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 the analysis of 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 via data cleaning, data integration, data mining, artificial intelligence, and pattern evaluation. FL can then manage the resulting information in terms of flow assurance and production optimisation. The same techniques can also be extended to the reservoir and the production network, for an integrated approach to production system analysis.

Item Type:Conference Proceedings
Additional Information:Proceedings - SPE Annual Technical Conference and Exhibition, pages 681-691.
Status:Published
Refereed:Yes
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

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