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.