Article

Dual-modality and dual-energy gamma ray densitometry of petroleum products using an artificial neural network

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Abstract

The prediction of volume fractions in order to measure the multiphase flow rate is a very important issue and is the key parameter of multi-phase flow meters (MPFMs). Currently, the gamma ray attenuation technique is known as one of the most precise methods for obtaining volume fractions. The gamma ray attenuation technique is based on the mass attenuation coefficient, which is sensitive to density changes; density is sensitive in turn to temperature and pressure fluctuations. Therefore, MPFM efficiency depends strongly on environmental conditions. The conventional solution to this problem is the periodical recalibration of MPFMs, which is a demanding task. In this study, a method based on dual-modality densitometry and artificial intelligence (AI) is presented, which offers the advantage of the measurement of the oil–gas–water volume fractions independent of density changes. For this purpose, several experiments were carried out and used to validate simulated dual modality densitometry results. The reference density point was established at a temperature of 20 °C and pressure of 1 bar. To cover the full range of likely density fluctuations, four additional density sets were defined (at changes of ±4% and ±8% from the reference point). An annular regime with different percentages of oil, gas and water at different densities was simulated. Four features were extracted from the transmission and scattered detectors and were applied to the artificial neural network (ANN) as inputs. The input parameters included the 241Am full energy peak, 137Cs Compton edge, 137Cs full energy peak and total scattered count, and the outputs were the oil and air percentages. A multi-layer perceptron (MLP) neural network was used to predict the volume fraction independent of the oil and water density changes. The obtained results show that the proposed ANN model achieved good agreement with the real data, with an estimated root mean square error (RMSE) of less than 3.

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... This is unsuitable for area-averaged measurements across the pipe cross section. For this purpose, broad beam gamma ray techniques are in use (Hanus et al., 2018;Nazemi et al., 2016;Roshani et al., 2015Roshani et al., , 2017. In the broad beam method, instead of a collimated beam, a diverging gamma beam covering the entire pipe diameter is used. ...
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Flow-induced vibration (FIV) is a common phenomenon observed in internal flows and is frequently encountered in technical systems like process plants, nuclear plants, oil-piping or heat exchangers. Compared to single-phase flows, FIV is more difficult to predict and analyze for internal two-phase flows. As a result, experimental data and analysis tools related to two-phase flow are limited to specific aspects or conditions. Another problem is that for real-world applications, FIV analysis is applied to multi-structural components, which becomes complicated due to the size of the technical systems. Thus, experimental studies are usually realized first within the laboratory using a prototype of the original structure. Besides experimental investigations, Computational Fluid Dynamics (CFD) is increasingly adopted and already a prevalent tool for FIV assessment. However, further development in CFD models and methods is necessary in order to complement the experimental database. Additionally, CFD is useful for enhanced understanding of fundamental aspects of two-phase flows, and for gaining insights from situations where experiments are difficult or infeasible, such as in deep-sea bore-wells, sub-sea riser pipelines, and in nuclear installations. It is also known that there is a lack of sufficiently accurate empirical correlations for terms related to mass, momentum, and energy transfer across the phases for two-phase flows, and CFD can be useful in this respect. Furthermore, for estimating the accuracy of CFD models, comparisons with benchmark results for two-phase, internal, multi-structural flows are necessary. Unfortunately, the experimental database involving internal two-phase flows is very limited, and this is a bottleneck for the development of computational techniques. The following contribution presents a review of the research on FIV involving two-phase internal flows with relevance to multi-structural components. Methodological literature for two-phase flow measurements along with the latest applications are put forth. Problem areas of two-phase FIV systems have been brought out, and future avenues of research for two-phase, internal FIV are identified. The following specific areas of two-phase FIV are reviewed. Two-phase FIV in subsea risers and in pipeline riser systems is discussed. The slug flow regime is analyzed in particular due its predominant impact on two-phase FIV. Parameters affecting two-phase FIV along with two-phase correlations are discussed. Power Spectrum Density (PSD) and Fourier transform applications for two-phase FIV form another section. Latest research efforts involving the two-way interaction of fluid and structure are presented. Both numerical and experimental works have been reviewed. The bulk of the important works for two-phase FIV is experimental in nature. Numerical models and computational power have not been developed enough for simulating more complex, multistructural flows. They are limited to simple cases involving simplified computational models. Experimental efforts for large multistructural components involve the initial use of prototypes and can prove to be costly for fully developed industrial-scale rigs. However, experimentation currently holds an irreplaceable position in two-phase FIV studies.
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... A few possibilities are the measurement of void fraction in two-phase flows independent of the flow regime, which is an essential parameter in oil production (Nazemi et al., 2016); the identification of changes in fluid flow properties that can interfere in the equipment used to measure multiphase flow in pipelines (Roshani et al., 2017); the identification of the interface region formed in the transport of oil by-products in pipelines (Salgado et al., 2020); or the identification of type and amount of oil by-products transported in pipelines, so that possible mixtures can be redirected to separation tanks (Roshani et al., 2020). Other examples can be found in the references Johansen et al., 2000;Holstad et al., 2005;Khabaz et al., 2015;and Roshani et al., 2015. In this study, a noninvasive method to determine the salt concentration in seawater was developed. ...
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... In [22], a method was proposed in order to determinate the void fraction in two-phase flow independent of density changes using ANN. In [23], it was shown that a combination of ANN and gamma-ray densitometer can be used to measure the volume fractions independent of density changes in multiphase flows. ...
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... Moreover, the optimization of three-phase flow metering setup using two transmission detectors have been reported using gamma-ray technique and ANN modeling in the literature (Roshani et al., 2017a). In all previous studies, different methods such as dual modality, multi-beam gamma-ray and etc. have been utilized to identify the regime and volume fraction in a static condition while they assumed that the temperature was fixed (Nazemi et al., 2014;Roshani et al., 2015). However, an experimental study was not reported yet to address the investigation of temperature effect on the two-phase flow systems in dynamic conditions. ...
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... In the ANFIS context, if the RMSE measure is not satisfactory, the adjustment of membership functions and the rule refinement procedure is activated towards better optimisation of the model. Therefore, the RMSE is used to evaluate the performance of ANFIS in this study, as shown in the following equation (Roshani, Feghhi, & Setayeshi, 2015;Eftekhari Zadeh, Feghhi, Roshani, & Rezaei, 2016): ...
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Gamma-ray densitometry is a frequently used non-intrusive method for determining void fraction in two- and multi-phase gas liquid pipe flows. The traditional gamma-ray densitometer using a 137Cs source and a scintillation PMT detector has proved itself reliable and robust. This paper presents a method using a low energy source (241Am), which offers the advantages of reduced size due to reduced shielding requirements, compact detectors, and lesser dependence on flow regime, due to its multibeam measurement configuration. These are important aspects with regard to future subsea and down-hole fluid flow measurement applications. The performance of single-beam and the compact multi-beam low-energy gamma-ray measurement principles was compared. Consideration of the measurement volume, defined by the detector area and the radiation beam, demonstrated the flow regime dependency of single-beam gamma-ray measurement principles. With the multi-beam low-energy gamma-ray measurement principle, the dependence on flow regime is negligible when several detector responses are combined. Use of phantoms and one movable detector verified the multi-beam gamma-ray measurement principle. The detector responses at several positions around the pipe were obtained for different flow regimes and void fractions.
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Recently, multiple input, single output, single hidden-layer feedforward neural networks have been shown to be capable of approximating a nonlinear map and its partial derivatives. Specifically, neural nets have been shown to be dense in various Sobolev spaces. Building upon this result, we show that a net can be trained so that the map and its derivatives are learned. Specifically, we use a result of Gallant's to show that least squares and similar estimates are strongly consistent in Sobolev norm provided the number of hidden units and the size of the training set increase together. We illustrate these results by an application to the inverse problem of chaotic dynamics: recovery of a nonlinear map from a time series of iterates. These results extend automatically to nets that embed the single hidden layer, feedforward network as a special case.
Conference Paper
The models of dual modality densitometry were developed. It can be used for measuring the gas volume fraction and water volume fraction in oil water gas pipe flow. The models are complex. In order to solve models, it often uses simplified models. This reduces measurement precision. The method of measuring gas and water volume fraction using neural networks was presented. The simulation data was gotten using Geant4. The radial basis function networks were trained and tested on computer simulation data. The results show that networks predicted gas volume fraction fit true gas fraction well and water volume fraction has some deviations.
Article
Dual mode densitometry is presented as a novel method of measuring the gas volume fraction in gas/oil/water pipe flows independent of the salinity of the water component. The different response in photoelectric attenuation and Compton scattering to changes in salinity is utilized. The total attenuation coefficient is found through traditional transmission measurements with a detector positioned outside the pipe wall diametrically opposite the source. The scatter response is measured with a second detector positioned somewhere between the source and the transmission detector. The feasibility of the method is demonstrated for homogeneously mixed flows.
Article
The Marquardt algorithm for nonlinear least squares is presented and is incorporated into the backpropagation algorithm for training feedforward neural networks. The algorithm is tested on several function approximation problems, and is compared with a conjugate gradient algorithm and a variable learning rate algorithm. It is found that the Marquardt algorithm is much more efficient than either of the other techniques when the network contains no more than a few hundred weights.