Abstract
A new method for the determination of oil and water flow rates in vertical upward oil-water two-phase pipe flows has been proposed. This method consists of an application of machine learning techniques on the probability density function (PDF) and the power spectral density (PSD) of the power spectrum output of an ultrasonic Doppler sensor in the pipe. The power spectrum characteristic parameters of the two-phase flow are first determined by the probability density function (PDF) method. Then, the transducer signal is preprocessed by distance correlation analysis (DCA), and independent features are extracted by principal component analysis (PCA). The extracted features are used as input to a least-squares fit, which gave the oil flow rates as output. In the same way, the transducer signal is also preprocessed by partial correlation analysis (PCA), and independent features were extracted using independent component analysis (ICA). The extracted features were used as inputs to multilayer back-propagation neural networks, which water cuts as output. The present method was used to calibrate an ultrasonic Doppler sensor to estimate the flow rates of both phases in oil–water flow in a vertical pipe of diameter 159 mm. Predictions of the present method were in good agreement with direct flow rate measurements. Compared to previously used methods of feature extraction from the ultrasonic Doppler power spectrum signals, the present method provides a theoretical basis for the interpretation of ultrasonic multiphase flow logging data. Ultrasonic multiphase flow logging has potential application value in the production profile logging and interpretation evaluation of production wells with low fluid production and high water cut.
Highlights
Ultrasonic multiphase flow logging technology solves the Geofluids problems of two-phase flow and three-phase flow logging in complex well conditions, and the logging data provides a reliable basis for oilfield development engineers [4]
For the flow rate prediction, the probability density function (PDF) and power spectral density (PSD) are first calculated from the input signal, and appropriate features are extracted by using principal component analysis (PCA)
The oil flow rate in oil-water two-phase flow can be accurately calculated by extracting principal component value of the characteristic parameters of the ultrasonic power spectrum, the main reason is that the total flow rate is small, the oil phase velocity is close to the static drift velocity of oil in water, and the change of oil flow rate mainly depends on the number of oil bubbles, which is strongly related to the amplitude of oil and area of oil peak
Summary
Production logging is a major means of oil well dynamic monitoring. It is an important issue in the logging industry to understand the production status of the oil well production layer, the remaining oil production of the reservoir, the evaluation of the reservoir reconstruction effect, and the adjustment and improvement of the development plan [1]. Ultrasonic multiphase flow logging technology solves the Geofluids problems of two-phase flow and three-phase flow logging in complex well conditions (low production fluid, sand out, highly deviated well, etc.), and the logging data provides a reliable basis for oilfield development engineers [4]. At this stage, the realization of the instrument method is relatively mature, and the difficulty lies in how to extract information that can accurately reflect the flow of oil, gas, and water from a single ultrasonic frequency spectrum data [5]. It provides a new interpretation method for the ultrasonic multiphase flow logging of oil-water two-phase production wells with low production liquids
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