Tag: Multiphase flow
Learning characteristic parameters and dynamics of centrifugal pumps under multiphase flow using physics-informed neural networks
Electrical submersible pumps (ESPs) are prevalently utilized as artificial lift systems in the oil and gas industry. These pumps frequently encounter multiphase flows comprising a complex mixture of hydrocarbons, water, and sediments. Such mixtures lead to the formation of emulsions, characterized by an effective viscosity distinct from that of the individual phases. Traditional multiphase flow meters, employed to assess these conditions, are burdened by high operational costs and susceptibility to degradation. To this end, this study introduces a physics-informed neural network (PINN) model designed to indirectly estimate the fluid properties, dynamic states, and crucial parameters of an ESP system. A comprehensive structural and practical identifiability analysis was performed to delineate the subset of parameters that can be reliably estimated through the use of intake and discharge pressure measurements from the pump. The efficacy of the PINN model was validated by estimating the unknown states and parameters using these pressure measurements as input data. Furthermore, the performance of the PINN model was benchmarked against the particle filter method utilizing both simulated and experimental data across varying water content scenarios. The comparative analysis suggests that the PINN model holds significant potential as a viable alternative to conventional multiphase flow meters, offering a promising avenue for enhancing operational efficiency and reducing costs in ESP applications.
Phase inversion identification in Electrical Submersible Pumps using mechanical vibrations
Electric Submersible Pumps (ESPs) are multistage centrifugal pumps used in the artificial lift and transport of multiphase fluid mixtures. The flow regime is a liquid–liquid flow when the fluids correspond to two non-miscible fluids. Liquid–liquid flow is a mixture with a continuous and dispersed phase. As the amount of fluid in the dispersed phase increases, the dispersed phase suddenly becomes continuous and vice-versa. This transition phenomenon is called phase inversion. The flow regimes in oil–water mixtures are oil-in-water (o/w) and water-in-oil (w/o) flow regimes. This work demonstrates a correlation between the flow regime and the flow-induced vibration (FIV) in ESP operating with an oil–water mixture. This research proposes a novelty method to flow regime identification based on the Root Mean Square (RMS) of the vibration acceleration of the Fast Fourier Transform (FFT) signal.
The experimental setup consists of an 8-stage Electrical Submersible Pump (ESP) and a vibration acquisition system with six accelerometers uniformly distributed along the ESP. The experimental procedure consists of changing the water cut (percentage of water) from the oil flow regime to the water flow regime, maintaining stable ESP rotational speed, the total flow rate, and the oil viscosity. For each water cut, mechanical vibration is collected. The operational conditions consider 30, 40, and 50 Hz rotational speeds and viscosities between 70 and 210 cP.
Frequency domain analysis involves studying FFT between 0 and 5000 Hz, considering different water cuts and frequency ranges. Statistical features – mean, variance, geometric mean harmonic mean, and RMS – were extracted from the FFT for each frequency range. Results showed a strong correlation between the RMS of FFT and the phase inversion phenomena considering the rotational speed. A logistic regression model was employed to establish a transition boundary between oil-in-water and water-in-oil using 10% of the data. The model successfully separated at least 95.67% of the remaining data in the least favorable scenario.