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Toward an Enhanced Bayesian Estimation Framework for Multiphase Flow Soft-sensingNormal access

Authors: X. Luo, R. Lorentzen, A. Stordal and G. Nævdal
Event name: ECMOR XIV - 14th European Conference on the Mathematics of Oil Recovery
Session: History Matching I
Publication date: 08 September 2014
DOI: 10.3997/2214-4609.20141782
Organisations: EAGE
Language: English
Info: Extended abstract, PDF ( 433.74Kb )
Price: € 20

Summary:
Smart wells are advanced operation facilities used in modern fields. Typically, a smart well is equipped with downhole sensors that collect and transmit, for instance, pressure and temperature data in order to monitor well and reservoir conditions in the field. For economical reasons, however, the number of downhole sensors is limited. Therefore, they may not be able to provide complete information about the properties of the fluids, e.g., the flow rates, in places other than the locations of the sensors. In order to evaluate fluid properties in the well, one needs to estimate them based on the collected data from the sensors. Such an exercise is often called soft sensing" or soft metering" (see, for examples, Bloemen et al., 2006; de Kruif et al., 2008; Leskens et al., 2008; Lorentzen et al., 2010; Wrobel and Schiferli, 2009). In this work the authors study the multiphase flow soft-sensing problem based on the framework used in Lorentzen et al. (2013). There are three functional modules in this framework, namely, a transient well flow model that describes the response of certain physical variables in a well, for instance, temperature and pressure, to the flow rates entering and leaving the well zones; a Markov jump process that is designed to capture the potential abrupt changes in the flow rates; and an estimation method that is adopted to estimate the underlying flow rates based on the measurements from downhole sensors. In Lorentzen et al. (2013), the variances of the flow rates in the Markov jump process are chosen manually. To fill this gap, in the current work two approaches are proposed in order to optimize the variance estimation. Through a numerical example, we show that,when the estimation framework is used in conjunction with these two proposed variance-estimation approaches, it can achieve reasonable performance in terms of matching both the measurements of the physical sensors and the true underlying flow rates. This abstract has partial overlaps with our work "Toward an enhanced Bayesian estimation framework for multiphase flow soft-sensing" that is going to appear in the special issue Bayesian methods in inverse problems of the journal Inverse Problems, 2014.


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