摘要：Dewatering gas wells typically exhibit a high gas–liquid ratio, making tapered electrical submersible pump (ESP) systems a common choice. However, the flow rate within the pump varies significantly along its length, and production parameters fluctuate considerably across different stages of operation for a gas reservoir. Traditional ESP sizing methods typically consider one single operating case and one single pump model. In contrast, tapered ESP systems require the designer to manually select and combine pump models, stage numbers, and operating frequencies based largely on experience. This process can be cumbersome and time-consuming. To address the limitations of existing ESP sizing methods, this study develops a computational program for ESP operation parameters stage by stage and generates extensive training data. A fully connected neural network (FCNN) based on the backpropagation (BP) algorithm is then trained on these data. The model can predict key parameters such as gas volume fraction (GVF) and flow rate along the pump, operating frequency, and total pump efficiency, using input data such as fluid parameters at the pump’s intake and discharge, as well as pump stage numbers and performance curve data. The model demonstrates high accuracy, with a mean absolute error (MAE) of 0.3431, a mean squared error (MSE) of 0.3231, and a coefficient of determination (R2) of 0.9991. By integrating a wellbore two-phase flow model and leveraging industry experience in pump sizing, a hybrid model for automatic ESP sizing under multiple working conditions is proposed, with the objective of maximizing pump efficiency. This model enables optimal pump sizing, calculates the operating frequency corresponding to given working cases, significantly reduces the workload of designers, and enhances the overall design outcomes. ? 2025 by the authors.

ISSN号：2227-9717

卷、期、页：卷13期4

发表日期：2025-04-01

影响因子：0.000000

期刊分区（SCI为中科院分区）：四区

收录情况：SCI（科学引文索引印刷版）,EI（工程索引）,SCIE（科学引文索引网络版）

发表期刊名称：Processes

通讯作者：姚劲松,王萌雨

第一作者：韩国庆,梁星原

论文类型：期刊论文

论文概要：姚劲松,韩国庆,梁星原,王萌雨，Machine Learning-Based Sizing Model for Tapered Electrical Submersible Pumps Under Multiple Operating Conditions，Processes，2025，卷13期4

论文题目：Machine Learning-Based Sizing Model for Tapered Electrical Submersible Pumps Under Multiple Operating Conditions