基于大数据的油气集输系统生产能耗时序预测模型

doi:10.7623/syxb2016S2020

石油学报 ›› 2016, Vol. 37 ›› Issue (S2): 158-164.DOI: 10.7623/syxb2016S2020

• 石油工程 • 上一篇

基于大数据的油气集输系统生产能耗时序预测模型

檀朝东¹, 项勇², 赵昕铭², 王辉萍¹, 高丽洁¹

1. 中国石油大学石油工程学院北京 102249;
2. 中国石油大港油田公司采油工艺研究院天津 300280

收稿日期:2016-07-18 修回日期:2016-12-13 出版日期:2016-12-30 发布日期:2017-03-08
通讯作者: 项勇,男,1968年6月生,1990年获石油大学(华东)学士学位,现为中国石油大港油田公司采油工艺研究院副总工程师、高级工程师,主要从事油气田地面集输系统方面研究。Email:dg_xiangyong@petrochina.com.cn
作者简介:檀朝东,男,1968年5月生,1990年获石油大学(华东)采油工程专业学士学位,2003年获石油大学(北京)机械设计专业博士学位,现为中国石油大学(北京)石油工程学院副研究员、硕士生导师,主要从事采油气工艺和石油工程大数据方面研究。Email:tanchaodong@cup.edu.cn

Energy consumption prediction and application in oil and gas gathering and transferring system production based on large data

Tan Chaodong¹, Xiang Yong², Zhao Xinming², Wang Huiping¹, Gao Lijie¹

1. College of Petroleum Engineering, China University of Petroleum, Beijing 102249, China;
2. Oil Production Technology Institute, PetroChina Dagang Oilfield Company, Tianjin 300280, China

Received:2016-07-18 Revised:2016-12-13 Online:2016-12-30 Published:2017-03-08

摘要/Abstract

摘要：

针对集输系统组成关系多、系统行为复杂、子系统之间以及系统与环境之间的关联程度高、耦合性强、易产生故障和能耗高等特点，基于油气集输生产过程中积累的温度、压力、流量、设备工作制度、能耗等海量数据，建立了集输数据粒度模型，实现了基于热能利用率、单位液量能耗等多目标、多变量时序的集输系统生产能耗预测。针对不同时间粒度（如日、月、年等）、不同空间粒度（如井组、区块、油田等）、不同集输方式粒度（如单相输、油-气-水混输），建立了多变量时序混沌能耗预测模型；构造了粒关联规则模式挖掘算法。以大港油田A集输系统为例，研究了集输生产系统的能耗因素粒之间的关联关系；预测了集输生产参数调整对系统未来能耗变化，获得集输系统效率和能耗的预警。

关键词: 集输系统, 混沌时序, 相空间重构, 粒关联规则, 能耗预测

Abstract:

The gathering system, which is prone to failure and high energy consumptions, consists of many different parts, perform complex system behaviors and has a high degree of correlation with the environment and strong coupling with each other.In the view of these characteristics, in this paper, based on the big data including temperature, pressure, flow rate, working system of the equipment and energy consumptions data accumulated during oil and gas gathering and transferring process, a granularity model of gathering and transferring data is set up to predict energy consumption with multiple targets like heat energy utilization efficiency, energy consumption per unit amount of liquid and multi-variable time sequence. A chaotic energy consumption forecasting model with multi-variable time sequence is established according to different time granularity(day, month and year, etc.), different space granularity(well group, block and field, etc.) and different ways of gathering and transferring(single-phase flow transferring and oil-gas-water three-phase flow transferring, etc.).Take a certain gathering system in Dagang oilfield as an example, the grain of association rule algorithm is constructed to study the relationship between the energy consumption factors of gathering system. Early warnings of gathering and transferring system efficiency and energy consumption are gained by predicting the production parameters.

Key words: gathering and transportation system, chaotic time series, phase-space reconstruction, grain of association rules, energy consumption prediction

中图分类号:

TE832.2

檀朝东, 项勇, 赵昕铭, 王辉萍, 高丽洁. 基于大数据的油气集输系统生产能耗时序预测模型[J]. 石油学报, 2016, 37(S2): 158-164.

Tan Chaodong, Xiang Yong, Zhao Xinming, Wang Huiping, Gao Lijie. Energy consumption prediction and application in oil and gas gathering and transferring system production based on large data[J]. Acta Petrolei Sinica, 2016, 37(S2): 158-164.

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基于大数据的油气集输系统生产能耗时序预测模型

Energy consumption prediction and application in oil and gas gathering and transferring system production based on large data

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