Electrical submersible pumps (ESP) are the second most used artificial lifting equipment in the oil and gas industry due to their high flow rates and boost pressures. They often have to handle multiphase flows, which usually contain a mixture of hydrocarbons, water, and/or sediments. Given these circumstances, emulsions are commonly formed. It is a liquid-liquid flow composed of two immiscible fluids whose effective viscosity and density differ from the single phase separately. In this context, accurate modeling of ESP systems is crucial for optimizing oil production and implementing control strategies. However, real-time and direct measurement of fluid and system characteristics is often impractical due to time constraints and economy. Hence, indirect methods are generally considered to estimate the system parameters. In this paper, we formulate a machine learning model based on Physics-Informed Neural Networks (PINNs) to estimate crucial system parameters. In order to study the efficacy of the proposed PINN model, we conduct computational studies using not only simulated but also experimental data for different water-oil ratios. We evaluate the state variable's dynamics and unknown parameters for various combinations when only intake and discharge pressure measurements are available. We also study structural and practical identifiability analyses based on commonly available pressure measurements. The PINN model could reduce the requirement of expensive field laboratory tests used to estimate fluid properties.

翻译：电潜泵(ESP)是油气行业中仅次于第二常用的人工举升设备，因其具有高排量和增压能力。这类设备常需处理多相流，通常包含碳氢化合物、水和/或沉积物的混合物。在此类工况下，常会形成乳液——由两种不相溶流体组成的液-液流动，其有效粘度与密度均不同于单相流体。在此背景下，精确建立ESP系统模型对于优化石油生产和实施控制策略至关重要。然而，受限于时间与经济因素，直接实时测量流体及系统特性参数往往不切实际。因此通常采用间接方法估计系统参数。本文基于物理信息神经网络(PINNs)提出一种机器学习模型，用于估计关键系统参数。为验证该PINN模型的效能，我们分别采用模拟数据与不同油水比的实验数据进行计算研究。在仅能获取入口与出口压力测量值的条件下，评估了多种工况组合下状态变量的动态特性与未知参数。基于常规可获取的压力测量数据，进一步开展了结构性与实用性可辨识性分析。该PINN模型可减少昂贵现场实验室测试的需求，从而有效估计流体物性参数。