Research of the Influence of Marine Residual Fuel Composition on Sedimentation Due to Incompatibility

Sultanbekov, Radel; Islamov, Shamil; Mardashov, D.V.; Белоглазов, И. И.; Hemmingsen, Tor

doi:10.3390/jmse9101067

Cited by 40 publications

(28 citation statements)

References 17 publications

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“…As was mentioned earlier, some authors noted that SSC decays over time so a 3probe-sampling procedure was implemented [32,33]. The figures below represent the ratio of SSC on stages 2-3 to the SSC on stage 1.…”

Section: Resultsmentioning

confidence: 98%

Experimental Evaluation of the Multiphase Flow Effect on Sand Production Process: Prepack Sand Retention Testing Results

et al. 2022

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This paper considers a field with high-viscosity oil in a terrigenous weakly consolidated reservoir (sandstone) with a massive gas cap and an aquifer. The major challenge in the field operation is the migration of solid sand particles into the well, accompanied by a breakthrough of water and/or gas. The goal of the investigation was to evaluate the degree of influence on the sanding process of water and gas shares in the produced fluid and to determine the effect of pressure drop. The research methodology is based on a series of filtration experiments on small-sized bulk reservoir models. Particle size distribution of bulk models was created in accordance with that of the reservoir. The experiments were made in the form of Prepack Sand Retention Tests (SRT). Gas breakthrough allows sand production on a relatively high level for a longer amount of time, even though the concentration of solids in the produced fluid is lower than that of water breakthrough. On the other hand, water breakthrough triggers higher sand production, but it rapidly decreases as time goes on. Retained permeability of the model-screen system from the drawdown pressure (pressure gradient) and phase distribution of the flow were investigated. Moreover, a methodology has been developed for conducting filtration tests on bulk reservoir models to evaluate the efficiency of different screens (wire-wrapped screens, in particular).

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Section: Resultsmentioning

confidence: 98%

Experimental Evaluation of the Multiphase Flow Effect on Sand Production Process: Prepack Sand Retention Testing Results

et al. 2022

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“…In the past few decades, various advanced computational methods have been applied in various fields of study such as chemical engineering [32][33][34][35][36][37], electrical and computer engineering [38][39][40][41], civil engineering [42][43][44], mechanical engineering [45][46][47][48][49][50][51], petroleum engineering [52][53][54][55][56][57][58][59][60][61][62][63], and environmental engineering [64,65], etc. The ANN has been demonstrated to be the most potent technique for classification and prediction among the aforementioned computational methods.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Extraction of Time-Domain Characteristics and Selection of Effective Features Using Correlation Analysis to Increase the Accuracy of Petroleum Fluid Monitoring Systems

et al. 2022

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In the current paper, a novel technique is represented to control the liquid petrochemical and petroleum products passing through a transmitting pipe. A simulation setup, including an X-ray tube, a detector, and a pipe, was conducted by Monte Carlo N Particle-X version (MCNPX) code to examine a two-by-two mixture of four diverse petroleum products (ethylene glycol, crude oil, gasoline, and gasoil) in various volumetric ratios. As the feature extraction system, twelve time characteristics were extracted from the received signal, and the most effective ones were selected using correlation analysis to present reasonable inputs for neural network training. Three Multilayers perceptron (MLP) neural networks were applied to indicate the volume ratio of three kinds of petroleum products, and the volume ratio of the fourth product can be feasibly achieved through the results of the three aforementioned networks. In this study, increasing accuracy was placed on the agenda, and an RMSE < 1.21 indicates this high accuracy. Increasing the accuracy of predicting volume ratio, which is due to the use of appropriate characteristics as the neural network input, is the most important innovation in this study, which is why the proposed system can be used as an efficient method in the oil industry.

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“…where x is the feature vector, v is the center position, σ is the spread, z j is the output prediction from the jth node of the output layer, u mj is weights, and b j is bias. It is worth mentioning that, in the past few decades, various advanced computational approaches, e.g., finite element, numerical linear algebra, statistics, numerical analysis, tensor analysis, and artificial intelligence, have been applied in various fields of study such as chemical engineering [26][27][28][29], electrical and computer engineering [30][31][32][33][34][35][36][37], civil engineering [38][39][40], mechanical engineering [41][42][43][44][45][46][47][48][49][50][51][52][53][54][55], petroleum engineering [56][57][58][59][60][61][62][63][64][65][66][67][68][69], environmental engineering [70,…”

Section: X-ray Tube Voltage Optimization Proceduresmentioning

confidence: 99%

Optimization of X-ray Tube Voltage to Improve the Precision of Two Phase Flow Meters Used in Petroleum Industry

et al. 2021

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To the best knowledge of the authors, in all the former studies, a fixed value of X-ray tube voltage has been used for investigating gas–liquid two-phase flow characteristics, while the energy of emitted X-ray radiations that depends on the tube voltage can significantly affect the measurement precision of the system. The purpose of present study is to find the optimum tube voltage to increase the accuracy and efficiency of an intelligent X-ray radiation-based two-phase flow meter. The detection system consists of an industrial X-ray tube and one detector located on either side of a steel pipe. Tube voltages in the range of 125–300 kV with a step of 25 kV were investigated. For each tube voltage, different gas volume percentages (GVPs) in the range of 10–90% with a step of 5% were modeled. A feature extraction method was performed on the output signals of the detector in every case, and the obtained matrixes were applied to the designed radial basis function neural networks (RBFNNs). The desired output of the networks was GVP. The precision of the networks in every voltage and every number of neurons in the hidden layer were obtained. The results showed that 225 kV tube voltage is the optimum voltage for this purpose. The obtained mean absolute error (MAE) for this case is less than 0.05, which demonstrates the very high precision of the metering system with an optimum X-ray tube voltage.

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Research of the Influence of Marine Residual Fuel Composition on Sedimentation Due to Incompatibility

Cited by 40 publications

References 17 publications

Experimental Evaluation of the Multiphase Flow Effect on Sand Production Process: Prepack Sand Retention Testing Results

Experimental Evaluation of the Multiphase Flow Effect on Sand Production Process: Prepack Sand Retention Testing Results

Extraction of Time-Domain Characteristics and Selection of Effective Features Using Correlation Analysis to Increase the Accuracy of Petroleum Fluid Monitoring Systems

Optimization of X-ray Tube Voltage to Improve the Precision of Two Phase Flow Meters Used in Petroleum Industry

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