利用被动声波测井资料估算井筒流体相组成变化

N. Mutovkin, D. Mikhailov, I. Sofronov
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引用次数: 1

摘要

提出了一种被动声波测井资料解释方法,以估计流体流入引起的井筒流体含率变化。该算法使用机器学习方法来分析声场,特别是由近井筒区域的油藏流动噪声产生的声场。该方法是利用数值模拟产生的声场来设计的。模拟结果表明,井筒共振对声场谱图和沿井筒方向的强度分布有显著影响。解释结果表明,本文提出的机器学习模型能够较准确地预测入水后某一区域的持水率。由于共振特性对其不太敏感,因此在进水间隔前的持水量预测精度较低。研究了受噪声污染的被动声波测井数据失真对模型学习和解释算法预测精度的影响。正如预期的那样,入水间隔前的持水率估计对信号干扰更为敏感。该被动声波测井资料解释方法的新颖之处在于利用声波噪声空间频率特征的共振结构来定位流入层段,并估计油水体积分数。正如我们的研究所示,这些共振包含了井筒流体中流体含率变化的清晰指纹,并且相应的信息可以通过机器学习算法进行解释。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Estimation of Fluid Phase Composition Variation Along the Wellbore by Analyzing Passive Acoustic Logging Data
We present an approach of passive acoustic logging data interpretation to estimate wellbore fluid holdup variation along the wellbore due to the fluid inflow. The algorithm uses machine learning methods for the analysis of acoustic fields generated, in particular, by flow noise in the reservoir near wellbore zone. The method is designed using acoustic fields generated by numerical simulations. The study of simulation results shows the significant influence of wellbore resonances on acoustic field spectrograms and on intensity distributions along the wellbore. The interpretation results demonstrate that the suggested machine learning model predicts water holdup in a zone after the water inflow with high accuracy. The predictions of water holdup before the water inflow interval are less accurate because resonance characteristics are less sensitive to them. We also studied the influence of passive acoustic logging data distortion by contaminating noise on the model learning and on prediction accuracy for the developed interpretation algorithm. As expected, the estimation of water holdup before the water inflow interval is more sensitive to signal interference. The novelty of the suggested approach to passive acoustic logging data interpretation is in using resonance structures of the acoustic noise spatial frequency characteristics to locate the inflow interval and to estimate the oil and water volume fractions. The resonances contain a clear fingerprint of the fluid holdup variation in wellbore fluid, as shown by our study, and the corresponding information can be interpreted by the machine learning algorithms.
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