本文提出了一种使用人工神经网络（ANN）来预测电潜泵（ESP）排出压力的新方法。使用从 40 个不同井收集的超过 12,000 个数据点的数据集来训练和测试具有不同输入参数的各种 ANN 模型。准确预测排放压力的能力可以尽早发现可能的异常情况。
数据采集。ESP 传感器数据的数据收集过程涉及收集 2019 年至 2023 年期间运行的 40 口井的数据，起始日期为 2019 年至 2022 年。这些数据包括完整论文中提供的关键参数。
数据清理和集成。执行数据清理过程以确保保持数据的准确性和可靠性。使用散点图可视化数据集中每个变量的分布，并识别并删除或平滑异常值。通过使用统计方法填充缺失值，或者如果缺失值的百分比显着，则通过从数据集中删除它们来解决缺失值。
This paper presents a novel approach using artificial neural networks (ANNs) to predict the discharge pressure of electrical submersible pumps (ESPs). A data set of more than 12,000 data points collected from 40 different wells was used to train and test various ANN models with different input parameters. The ability to predict discharge pressure accurately can lead to the early detection of possible anomalies.
Data Collection. The data-collection process for ESP sensor data involved collecting data from 40 wells operational during 2019–2023 with starting dates from 2019 through 2022. The data included key parameters provided in the complete paper.
Data Cleaning and Integration. The data-cleaning process was performed to ensure that the accuracy and reliability of the data were maintained. Scatter plots were used to visualize the distribution of each variable in the data set, and outliers were identified and removed or smoothed. Missing values were addressed by fill-in using statistical methods or by removing them from the data set if the percentage of missing values was significant.
