contributor author | Dongyin Yan | |
contributor author | Haiyang Yu | |
contributor author | Yunlu Ma | |
contributor author | Yi Guo | |
contributor author | Zhuochao Li | |
contributor author | Fengyuan Yan | |
contributor author | Yongtu Liang | |
date accessioned | 2024-12-24T10:00:59Z | |
date available | 2024-12-24T10:00:59Z | |
date copyright | 11/1/2024 12:00:00 AM | |
date issued | 2024 | |
identifier other | JPSEA2.PSENG-1628.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4298137 | |
description abstract | In the process of oil–gas mixing transportation, a too-high gas–oil ratio (GOR) will lead to instable flow pattern and high pipeline pressure, which has great safety risks. Therefore, it is important to determine the maximum GOR. At present, it relies mainly on commercial software to simulate the operation of the mixture transportation pipeline and the hydrothermal operating parameters to determine the maximum GOR under certain condition. This enumeration method is time-consuming and does not apply to continuously parameters. To solve this problem, a data-driven predictive model is developed. The new features are constructed by analyzing the factors influencing the transportable maximum gas–oil ratio (TMGOR), and the highly correlated features are selected from them as the new features. After analyzing the characteristics of the target variables, data mapping is performed, and the processed data set is fed into a neural network for training to obtain a data-driven predictive model of TMGOR. Finally, the validation is carried out with field data from an oilfield block in northwest China. The results showed that the average relative error of the model does not exceed 8.2% compared with the simulation results of commercial software, which has a high accuracy and can provide a rationale for the decision-making of mixed transfer in the field. | |
publisher | American Society of Civil Engineers | |
title | Data-Driven Method for Predicting the Transportable Maximum Gas–Oil Ratio | |
type | Journal Article | |
journal volume | 15 | |
journal issue | 4 | |
journal title | Journal of Pipeline Systems Engineering and Practice | |
identifier doi | 10.1061/JPSEA2.PSENG-1628 | |
journal fristpage | 04024036-1 | |
journal lastpage | 04024036-8 | |
page | 8 | |
tree | Journal of Pipeline Systems Engineering and Practice:;2024:;Volume ( 015 ):;issue: 004 | |
contenttype | Fulltext | |