Soft Sensing for Gas-Condensate Field Production Using Parallel-Genetic-Algorithm-Based Data Reconciliation

2019 ◽  
Vol 19 (4) ◽  
Author(s):  
Dan Wang ◽  
Jing Gong ◽  
Qi Kang ◽  
Di Fan ◽  
Juheng Yang
Keyword(s):  
Genetic Algorithm ◽  
Engineering Application ◽  
Gas Condensate ◽  
Physical Models ◽  
Data Reconciliation ◽  
Parallel Genetic Algorithm ◽  
Two Phase ◽  
Soft Sensing ◽  
Flow Models ◽  
Conventional Optimization

During present offshore gas-condensate production, multiphase flow-meters, due to its exceedingly high cost, are being substituted by a soft sensing (SS) technique for estimating total and single-well flowrates through sensor measurements and physical models. In this work, the inverse problem is solved by data reconciliation (DR), minimizing weighted sum of errors with constraints integrating multiple two-phase flow models. The DR problem is solved by parallel genetic algorithm (PGA) without complex calculations required by conventional optimization. The newly developed SS method is tested by data from a realistic gas-condensate production system. The method is proved of good accuracy and robustness with invalid individual pressure sensor or unavailable total flowrate measurements. Meanwhile, the proposed method shows good parallel performance and the time cost of each DR process can meet the demand of engineering application.

Application of Genetic-Algorithm-Based Data Reconciliation on Offshore Virtual Flow Metering of Gas-Condensate Field Production

2018 ◽  
Author(s):  
Dan Wang ◽  
Jing Gong ◽  
Di Fan ◽  
Guoyun Shi ◽  
Juheng Yang
Keyword(s):  
Genetic Algorithm ◽  
Flow Rate ◽  
Engineering Application ◽  
Gas Condensate ◽  
Physical Models ◽  
Data Reconciliation ◽  
Production Flow ◽  
Two Phase ◽  
Flow Metering ◽  
Single Well

During present offshore gas-condensate production, flow meters, due to its exceedingly high cost, are being substituted by Virtual Flow Metering (VFM) Technology for monitoring total and single-well flow rates through sensor measurements and physical models. In this work, the inverse problem is solved by Data Reconciliation (DR), minimizing weighted sum of errors with constraints integrating multiple two-phase flow models. The DR problem is solved by Parallel Genetic Algorithm, without complex calculations required by conventional optimization. The newly developed VFM method is tested by data from a realistic gas-condensate production system. The results show good accuracy for the total mass flow rate with model calibration. Meanwhile, recommended single-well flow rate can be provided without physical meters. The method is proved of good robustness with individual pressure sensor invalid, even total flow rate measurements unavailable. The time cost of each reconciliation process can meet the demand of engineering application.

Computational Analysis of Downcomer Boiling Phenomena Using a Component Thermal Hydraulic Analysis Code, CUPID

2010 ◽  
Vol 133 (5) ◽  
Author(s):  
Hyoung Kyu Cho ◽  
Byong Jo Yun ◽  
Ik Kyu Park ◽  
Jae Jun Jeong
Keyword(s):  
Computational Analysis ◽  
Nuclear Reactor ◽  
Three Dimensional ◽  
Reactor Vessel ◽  
Physical Models ◽  
Low Flow ◽  
Two Phase ◽  
Rate Condition ◽  
Flow Models ◽  
Loss Of Coolant Accident

For the analysis of transient two-phase flows in nuclear reactor components such as a reactor vessel, a steam generator, and a containment, KAERI has developed a three-dimensional thermal hydraulic code, CUPID. It adopts a three-dimensional, transient, two-phase and three-field model and includes various physical models and correlations of the interfacial mass, momentum, and energy transfer for the closure. In the present paper, the CUPID code and its two-phase flow models were assessed against the downcomer boiling experiment, which was performed to simulate the downcomer boiling phenomena. They may happen in the downcomer of a nuclear reactor vessel during the reflood phase of a postulated loss of coolant accident. The stored energy release from the reactor vessel to the liquid inside the downcomer causes the boiling on the wall, and it can reduce the hydraulic head of the accumulated water, which is the driving force of water reflooding to the core. The computational analysis using the CUPID code showed that it can appropriately predict the multidimensional boiling phenomena under a low pressure and low flow rate condition with modification of the bubble size model.

THE MECHANISM OF RAISING AND QUANTIFICATION OF SPECIFIC HEAT FLUX AT BOILING OF NANOFLUIDS IN FREE CONVECTION CONDITIONS

2017 ◽  
pp. 25-34
Author(s):  
V.N. Moraru
Keyword(s):  
Heat Transfer ◽  
Heat Flux ◽  
Heat Capacity ◽  
Specific Heat ◽  
Chemical Properties ◽  
Heating Surface ◽  
Physical Models ◽  
Superheated Liquid ◽  
Two Phase ◽  
Boiling Process

The results of our work and a number of foreign studies indicate that the sharp increase in the heat transfer parameters (specific heat flux q and heat transfer coefficient _) at the boiling of nanofluids as compared to the base liquid (water) is due not only and not so much to the increase of the thermal conductivity of the nanofluids, but an intensification of the boiling process caused by a change in the state of the heating surface, its topological and chemical properties (porosity, roughness, wettability). The latter leads to a change in the internal characteristics of the boiling process and the average temperature of the superheated liquid layer. This circumstance makes it possible, on the basis of physical models of the liquids boiling and taking into account the parameters of the surface state (temperature, pressure) and properties of the coolant (the density and heat capacity of the liquid, the specific heat of vaporization and the heat capacity of the vapor), and also the internal characteristics of the boiling of liquids, to calculate the value of specific heat flux q. In this paper, the difference in the mechanisms of heat transfer during the boiling of single-phase (water) and two-phase nanofluids has been studied and a quantitative estimate of the q values for the boiling of the nanofluid is carried out based on the internal characteristics of the boiling process. The satisfactory agreement of the calculated values with the experimental data is a confirmation that the key factor in the growth of the heat transfer intensity at the boiling of nanofluids is indeed a change in the nature and microrelief of the heating surface. Bibl. 20, Fig. 9, Tab. 2.

Research on text feature clustering based on an improved parallel genetic algorithm

2015 ◽  
Author(s):  
M. Y. Jiang ◽  
X. J. Fan ◽  
Y. X. Zhou ◽  
J. Lian ◽  
J. Q. Jiang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Parallel Genetic Algorithm ◽  
Feature Clustering ◽  
Text Feature

scholarly journals Port-Hamiltonian formulation of two-phase flow models

2021 ◽  
Vol 149 ◽  
pp. 104881
Author(s):  
H. Bansal ◽  
P. Schulze ◽  
M.H. Abbasi ◽  
H. Zwart ◽  
L. Iapichino ◽  
...  
Keyword(s):  
Two Phase Flow ◽  
Hamiltonian Formulation ◽  
Phase Flow ◽  
Two Phase ◽  
Flow Models
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