Three feature extraction methods of sucker-rod pump indicator card data have been studied, simulated, and compared in this paper, which are based on Fourier Descriptors (FD), Geometric Moment Vector (GMV), and Gray Level Matrix Statistics (GLMX), respectively. Numerical experiments show that the Fourier Descriptors algorithm requires less running time and less memory space with possible loss of information due to nonoptimal numbers of Fourier Descriptors, the Geometric Moment Vector algorithm is more time-consuming and requires more memory space, while the Gray Level Matrix Statistics algorithm provides low-dimension feature vectors with more time consumption and more memory space. Furthermore, the characteristic of rotational invariance, both in the Fourier Descriptors algorithm and the Geometric Moment Vector algorithm, may result in improper pattern recognition of indicator card data when used for sucker-rod pump working condition diagnosis.
A series of experiments were conducted to investigate the flow pattern transitions and water holdup during oil–water–gas three-phase flow considering both a horizontal section and a vertical section of a transportation pipe simultaneously. The flowing media were white mineral oil, distilled water, and air. Dimensionless numbers controlling the multiphase flow were deduced to understand the scaling law of the flow process. The oil–water–gas three-phase flow was simplified as the two-phase flow of a gas and liquid mixture. Based on the experimental data, flow pattern maps were constructed in terms of the Reynolds number and the ratio of the superficial velocity of the gas to that of the liquid mixture for different Froude numbers. The original contributions of this work are that the relationship between the transient water holdup and the changes of the flow patterns in a transportation pipe with horizontal and vertical sections is established, providing a basis for judging the flow patterns in pipes in engineering practice. A dimensionless power-law correlation for the water holdup in the vertical section is presented based on the experimental data. The correlation can provide theoretical support for the design of oil and gas transport pipelines in industrial applications.
Water-cut detection of crude oil based on capacitive sensors and conductivity sensors are widely used in practice. However, the above two methods cannot achieve full-scale measurement of water-cut of crude oil. Besides, their measurement accuracy is sensitive to temperature, which greatly limits their application. A novel method of crude oil water-cut detection based on multi-sensor fusion is proposed in the paper. The proposed method uses a capacitive sensor and a conductivity sensor to measure the crude oil separately, and uses a temperature sensor to compensate the measurement result at the same time. Finally, the proposed method introduces a neural network for data fusion to predict the water content. The experiment results show that the proposed multi-sensor fusion technique performs better than capacitance and conductivity technique. The accuracy of this method is higher than single capacitance or conductivity method. When the water content is lower than 3%, the prediction error is less than 0.1%. When the water content is in a range from 3% to 10%, the prediction error is less than 0.5%. When the water content is in a range from 10% to 100%, the prediction error is less than 1.5%.
A series of experiments were conducted to investigate flow pattern transitions and concentration distribution during simultaneous pipe flow of oil–water two-phase flow through the horizontal and vertical sections. The flowing media applied were white mineral oil and distilled water. Superficial oil and water velocities were between 0 and 0.57 m/s. Flow pattern maps revealed that the horizontal and vertical sections of the pipe lead to different flow pattern characteristics under the same flow conditions. The original contributions of this work are that a transition mechanism for predicting the boundary between oil-in-water (O/W) flow and water-in-oil (W/O) in oil–water two-phase flow was obtained. The effects of input water cut, oil and water superficial velocities on the concentration distribution of the dispersed phase were studied. The empirical formulas for the phase holdup based on the drift-flux model were obtained. The predicted results agreed well with those of the experimental data, especially for the O/W flow pattern.
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