本文選題：油氣水三相流　切入點(diǎn)：能量解調(diào)　出處：《燕山大學(xué)》2015年碩士論文

【摘要】：多相流動(dòng)體系在工業(yè)生產(chǎn)中是非常熱門的一個(gè)研究方向,其中流型和各個(gè)流型下流量的預(yù)測(cè)是一項(xiàng)重要的研究?jī)?nèi)容。由于油氣水多相流信息信號(hào)具有難以預(yù)測(cè)的流動(dòng)特性,所以該方向的研究難度比較大。本文利用能量解調(diào)算法對(duì)油氣水三相流信號(hào)進(jìn)行特征提取,采用遺傳優(yōu)化神經(jīng)網(wǎng)絡(luò)構(gòu)建軟測(cè)量模型為準(zhǔn)確預(yù)測(cè)流型和流量提供了一種可行的方法。首先,針對(duì)油氣水三相流特征提取難度比較大、準(zhǔn)確性不高的問(wèn)題,本文重點(diǎn)研究了能量解調(diào)算法應(yīng)用的原理,將優(yōu)化后的算法應(yīng)用于油氣水三相流的特征提取上。該算法能從油氣水三相流信號(hào)中提取四個(gè)特征參數(shù)來(lái)表征油氣水三相流信號(hào)的主要特征。此外實(shí)驗(yàn)結(jié)果表明該算法在特征提取上具有很好的效果。其次,針對(duì)油氣水三相流流型和流量預(yù)測(cè)的問(wèn)題,本文將遺傳算法與神經(jīng)網(wǎng)絡(luò)算法相結(jié)合構(gòu)建出具有高質(zhì)量的軟測(cè)量模型。利用遺傳算法來(lái)優(yōu)化神經(jīng)網(wǎng)絡(luò)算法的權(quán)值和閾值從而達(dá)到提高軟測(cè)量模型準(zhǔn)確性的目的。從性能分析上來(lái)看該軟測(cè)量模型的使用能夠極大的提升軟測(cè)量在流型和流量預(yù)測(cè)上的準(zhǔn)確性。最后,基于油田信號(hào)的采集、多種特征提取算法和油氣水三相流軟測(cè)量模型,本論文利用能量解調(diào)方法為主要特征提取算法并結(jié)合其他特征提取方法,構(gòu)造出的特征向量訓(xùn)練樣本,來(lái)訓(xùn)練遺傳優(yōu)化的神經(jīng)網(wǎng)絡(luò)軟測(cè)量模型,實(shí)驗(yàn)結(jié)果充分證明該軟測(cè)量模型的性能有較大的提升。將該軟測(cè)量模型應(yīng)用于流型的識(shí)別,然后用該模型對(duì)油氣水三相流在各個(gè)流型下的油相流量和水相流量進(jìn)行預(yù)測(cè),從實(shí)驗(yàn)結(jié)果中得出該軟測(cè)量模型具有很好的預(yù)測(cè)能力。
[Abstract]:Multiphase flow system is a very popular research field in industrial production. The prediction of flow pattern and flow under each flow pattern is an important research content. Because the information signal of oil-gas-water multiphase flow has unpredictable flow characteristics, In this paper, the energy demodulation algorithm is used to extract the characteristics of oil-gas-water three-phase flow signal. Using genetic optimization neural network to construct soft sensor model provides a feasible method for accurate prediction of flow pattern and flow rate. First of all, it is difficult to extract the characteristics of oil-gas-water three-phase flow and the accuracy is not high. This paper focuses on the principle of the application of energy demodulation algorithm. The optimized algorithm is applied to the feature extraction of oil-gas-water three-phase flow. This algorithm can extract four characteristic parameters from the oil-gas-water three-phase flow signal to characterize the main characteristics of oil-gas-water three-phase flow signal. In addition, the experimental results show that the main characteristics of oil-gas-water three-phase flow signal can be represented by the algorithm. The algorithm has a good effect on feature extraction. Secondly, Aiming at the problem of flow pattern and flow prediction of oil-gas-water three-phase flow, This paper combines genetic algorithm with neural network algorithm to construct a soft sensor model with high quality. Genetic algorithm is used to optimize the weights and thresholds of neural network algorithm to improve the accuracy of soft sensor model. In terms of performance analysis, the use of the soft sensor model can greatly improve the accuracy of flow pattern and flow prediction in soft sensing. Finally, Based on oil field signal acquisition, various feature extraction algorithms and soft sensing model of oil-gas-water three-phase flow, this paper uses energy demodulation method as the main feature extraction algorithm and combines other feature extraction methods to construct feature vector training samples. The experimental results show that the performance of the soft sensor model has been greatly improved. The soft sensor model is applied to flow pattern recognition. Then, the model is used to predict the oil-phase flow and water-phase flow of the oil-gas-water three-phase flow under various flow patterns, and it is concluded from the experimental results that the soft-sensing model has a good predictive ability.
【學(xué)位授予單位】：燕山大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TE31;O359

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