This article presents an integrated method for estimating parameters for an electric submersible pump system with process variables data. Validation of a phenomenological model is also performed. The parameters and the associated probability density function are obtained through Bayesian inference, and the model validation is achieved in two stages. The first one is the validation of the dynamic response in which the model is compared with the experimental data. The second is achieved by comparing the regions covered by the experimental data and the model, both in steady-state. The experimental data's uncertainty is assessed using the Guide for the Expression of Uncertainty in Measurement. In turn, the uncertainty of the model's prediction is obtained by propagating the probability density function parameters. The results indicate that the method can provide a model to represent the system behavior within the existing uncertainties. Additionally, the procedure can be applied in oil production fields to provide substitute models for general purposes, such as production control, optimization, and assistance.

本文介绍了一种使用过程变量数据估算电动潜水泵系统参数的集成方法。还进行了现象学模型的验证。通过贝叶斯推理获得参数和相关的概率密度函数，并通过两个阶段实现模型验证。第一个是动态响应的验证，其中将模型与实验数据进行比较。第二个是通过比较稳态下的实验数据和模型所覆盖的区域来实现的。使用测量不确定度表示指南评估实验数据的不确定性。反过来，通过传播概率密度函数参数来获得模型预测的不确定性。结果表明，该方法可以为现有不确定性下的系统行为提供模型。此外，该程序可以应用于石油生产领域，以提供通用模型（例如生产控制，优化和辅助）的替代模型。