Temperature Field Prediction of a Multilayered Composite Pipeline Based on the Particle Filter Method

Abstract

Thermal management of subsea oil production systems in deep water environments is one of the main issues for petroleum exploitation operations. Thermal monitoring is crucial to avoid and control the formation of solid deposits, which in adverse operating conditions can result in blockages inside the production systems and consequently incur large financial losses. This paper aims to demonstrate the robustness of a Bayesian approach for accurate estimation of the produced fluid temperature field in a typical multilayered composite pipeline. The physical problem consists of a pipeline represented by a circular domain filled by a stagnant fluid (petroleum) with temperature dependent thermal properties, which is bounded by a multilayered composite pipe wall. The mathematical model governing the heat conduction problem in the multilayered wall and in the stagnant fluid was solved with the finite volume method. The Particle Filter method was used for the solution of the inverse transient problem involving the prediction of the temperature field in the medium, from limited temperature data available at one single location in the pipeline composite wall. The aim of this method is to represent the required posterior density function by a set of random samples with associated weights, and to compute the estimates based on these samples and weights. Results are presented in this paper by taking into account uncertainties in the state evolution and measurement models. Simulated temperature data is used in the inverse analysis for typical conditions observed during production shutdown periods.

State estimation in bioheat transfer: a comparison of particle filter algorithms

“…Such Monte Carlo methods, usually denoted as particle filters, can be readily applied to either linear or non-linear models, with Gaussian or non-Gaussian errors (Andrieu et al , 2004a, 2004b; Arulampalam et al , 2001; Carpenter et al , 1999; Doucet et al , 2000, 2001; Johansen and Doucet, 2008; Kaipio et al , 2005; Liu and Chen, 1998; Liu and West, 2001; Del Moral and Jasra, 2007; Del Moral et al , 2006; Orlande et al , 2012; Ristic et al , 2004; Sheinson et al , 2014). The application of particle filters to the solution of inverse problems of state estimation in heat transfer can be found in references (Andrade et al , 2014; Bermeo Varon et al , 2015; Colaço et al , 2012; Lamien et al , 2014, 2015, 2016; Orlande et al , 2012; Silva et al , 2014; Varon et al , 2015; Vianna et al , 2010).…”

State Estimation Problems in Heat Transfer

“…In this section, we apply the Bayesian filters described above to state estimation problems in heat transfer that have been recently addressed by our group. These problems include: (i) the estimation of a position-dependent transient heat source in a plate [26]; (ii) the estimation of the temperature field in oil pipelines [29]; (iii) the estimation of a transient line source and the solidification front in a phase-change problem [24]; and (iv) the estimation of the transient boundary heat flux in a natural convection problem [25]. For all cases, simulated temperature measurements were used in the inverse analysis.…”

“…In this paper, we present the application of the Kalman filter and of two different algorithms of the Particle filter, namely the sampling importance resampling and auxiliary sampling importance resampling [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18], to state estimation problems in heat transfer [19][20][21][22][23][24][25][26][27][28][29][30]. Before focusing on the applications of interest, the state estimation problem is defined and the Kalman and particle filters are described.…”

Numerical study and sensitivity analysis of two-phase oil-water flow and heat transfer in different flowline orientations using OpenFOAM

“…Flow assurance in the petroleum industry is one of the biggest problems bedeviling hydrocarbon production and this can cause serious challenges to subsea deepwater field developments [8][9][10]. This type of environment is characterized by low seabed temperature and high hydrostatic pressures, which may influence the flow of the produced hydrocarbon fluids (oil, gas, and water) through the flowline up to the processing facilities [8,9]. In addition, the flowline has sections with different orientations, usually between − 10 o and +10 o due to uneven seabed which affects the flow regimes [11,12] (Fig.…”

“…The subsea heat management system is very important to the successful operations of flow assurance in deepwater petroleum fields [8,9,13,14]. It is required during design to maintain the temperature of the fluid inside the flowlines well above the surrounding temperature.…”